System and method of a requirement, compliance and resource management

ABSTRACT

A system and/or a method based on a scalable requirement, compliance and resource management methodology for designing a product/service, optimizing relevant processes and enhancing real time and/or near real time collaboration between many users is disclosed. Utilizing, a learning (self-learning) computer, a requirement, compliance and resource management methodology is further integrated with (a) a machine learning/fuzzy/neuro-fuzzy logic algorithm and/or (b) statistical algorithm and/or (c) weighting logic algorithm and/or (d) game theory algorithm and/or (e) a blockchain and enhanced with a graphical user interface.

CROSS REFERENCE OF RELATED APPLICATIONS

The present application is a continuation-in-part (CIP) of

-   -   (a) U.S. Non-Provisional patent application Ser. No. 14/544,314        entitled, “SYSTEM AND METHOD OF A REQUIREMENT, COMPLIANCE AND        RESOURCE MANAGEMENT” filed on Dec. 22, 2014,    -   (b) which (a) is a continuation-in-part of U.S. Non-Provisional        patent application Ser. No. 13/815,843 entitled, “SYSTEM AND        METHOD OF A REQUIREMENT, COMPLIANCE AND RESOURCE MANAGEMENT”        filed on Mar. 15, 2013 (which claims the benefit of priority to        U.S. Provisional Patent Application No. 61/848,015 entitled,        “SYSTEM AND METHOD OF A REQUIREMENT, COMPLIANCE AND RESOURCE        MANAGEMENT METHODOLOGY” filed on Dec. 19, 2012),    -   (c) which (b) is a continuation-in-part of U.S. Non-Provisional        patent application Ser. No. 13/573,634 entitled, “SYSTEM AND        METHOD OF A REQUIREMENT, COMPLIANCE AND RESOURCE MANAGEMENT”        filed on Sep. 28, 2012.

The entire contents of all Non-Provisional patent applications andProvisional patent applications as listed in the previous paragraph andthe filed Application Data Sheet (ADS) are hereby incorporated byreference.

FIELD OF THE INVENTION

The present invention is related to a system and/or a method based on ascalable requirement, compliance and resource management methodology.

The requirement, compliance and resource management methodology of thepresent invention is intended for (a) designing a product/service, (b)scoping end-to-end process steps, which are required for designing theproduct/service, (c) identifying critical constrains for designing theproduct/service, (d) optimizing relevant processes for designing theproduct/service, (e) evaluating requirement specifications of eachprocess step for designing the product/service, (f) allocating resources(human capital and/or investment capital) for each process step fordesigning the product/service and (g) enhancing near real time and/orreal time collaboration between users.

DESCRIPTION OF PRIOR ART

One currently available product IBM Rational DOORS® software programenables to capture, trace, analyze and manage changes to requirements.

IBM Rational DOORS® can demonstrate compliance to regulations andstandards.

IBM Rational DOORS® software allows all stakeholders to activelyparticipate in the requirements process. It has ability to managechanging requirements with scalability. Its life cycle traceability canhelp teams align the methods and processes and also measure the impactof such methods and processes.

BACKGROUND OF THE INVENTION

In sharp contrast to IBM Rational DOORS®, the requirement, complianceand resource management methodology of the present invention is uniquelyenhanced with mathematical algorithms (e.g., fuzzy logic, statistics andweighting logic) to account for any inherent approximation, variabilityand uncertainty in a process step and/or all cumulative process steps.

Above is a significant innovation compared to IBM Rational DOORS®.

Furthermore, the requirement, compliance and resource managementmethodology of the present invention synthesizes optimization ofrelevant process steps, requirements, resources and critical constraintsfor near real time and/or real time collaboration.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 (schematic diagram) describes various applications of therequirement, compliance and resource management methodology.

FIG. 2 (schematic diagram) describes the connectivity (both one-way andtwo-way connectivity) of the requirement, compliance and resourcemanagement methodology (located at an enterprise server) with otherexternal systems and/or devices.

FIG. 3 (schematic diagram) describes the connectivity (both one-way andtwo-way connectivity) of the requirement, compliance and resourcemanagement methodology (located at a cloud server) with other externalsystems and/or devices.

FIG. 4 (schematic diagram) describes the connectivity (two-wayconnectivity) of the requirement, compliance and resource managementmethodology with users for (a) near real time and/or real timecollaboration between users, (b) product development, (c) procurement,system/test/QA engineering, (d) legal/compliance requirement/management,(e) product management, (f) product marketing, (g) technical support,(h) financial management and (i) executive management.

FIG. 5A (block diagram) describes one embodiment of the requirement,compliance and resource management methodology 100.

FIG. 5B consists of FIG. 5B1 and FIG. 5B2. FIG. 5C consists of FIG. 5C1and FIG. 5C2. FIG. 5E consists of FIG. 5E1 and FIG. 5E2. FIG. 5Fconsists of FIG. 5F1 and FIG. 5F2.

FIGS. 5B (schematic chart), 5C (schematic chart), 5D (schematic chart)and 5E (schematic chart) describe various embodiments of 100D of therequirement, compliance and resource management methodology 100 in FIG.5A. FIGS. 5E (schematic chart) and 5F (schematic chart) describe variousembodiments of 100A1 of the requirement, compliance and resourcemanagement methodology 100 in FIG. 5A.

Tables 6A, 6B, 6C, 6D and 6E describe the features and benefits of therequirement, compliance and resource management methodology 100, asdescribed in FIG. 5A. Features and benefits Table 6A describes specificfeatures and benefits of 100A of the requirement, compliance andresource management methodology 100 in FIG. 5A. Features and benefitsTable 6B describes specific features and benefits of 100A, 100B, 100Cand 100D of the requirement, compliance and resource managementmethodology 100 in FIG. 5A. Features and benefits Table 6C describesspecific features and benefits of 100D and 100E of the requirement,compliance and resource management methodology 100 in FIG. 5A. Featuresand benefits Table 6D describes specific features and benefits of 100Fof the requirement, compliance and resource management methodology 100in FIG. 5A. Features and benefits Table 6E describes specific featuresand benefits of 100F and 100A1 of the requirement, compliance andresource management methodology 100 in FIG. 5A.

FIG. 7A (block diagram) describes another embodiment of the requirement,compliance and resource management methodology 120, further enhanced bya question/answer format of a requirement input module and a fuzzy logicalgorithm module.

FIGS. 7B (schematic diagram) and 7C (schematic diagram) describe anapplication of the fuzzy logic module of the requirement, compliance andresource management methodology 120, as described in FIG. 7A.

FIG. 7D describes a fuzzy logic membership function.

FIG. 7E describes a decision flow chart of the fuzzy logic algorithmmodule of the requirement, compliance and resource managementmethodology 120, as described in FIG. 7A.

Tables 8A, 8B, 8C, 8D and 8E describe the features and benefits of therequirement, compliance and resource management methodology 120, asdescribed in FIG. 7A. Features and benefits Table 8A describes specificfeatures and benefits of 100A of the requirement, compliance andresource management methodology 120 in FIG. 7A. Features and benefitsTable 8B describes specific features and benefits of 100B, 100C and 100Dof the requirement, compliance and resource management methodology 120in FIG. 7A. Features and benefits Table 8C describes specific featuresand benefits of 100D, 100E and 100F of the requirement, compliance andresource management methodology 120 in FIG. 7A. Features and benefitsTable 8D describes specific features and benefits of 100F of therequirement, compliance and resource management methodology 120 in FIG.7A. Features and benefits Table 8E describes specific features andbenefits of 100F, 100A1, 100C1 and 100F1 of the requirement, complianceand resource management methodology 120 in FIG. 7A.

FIG. 9A (block diagram) describes another embodiment of the requirement,compliance and resource management methodology 140, further enhanced bya question/answer format of requirement input, a fuzzy logic algorithmmodule, a statistical algorithm module and a weighting logic algorithmmodule.

FIG. 9B describes an application of the statistical module of therequirement, compliance and resource management methodology 140, asdescribed in FIG. 9A.

FIG. 9C (statistical distribution plot), 9D (statistical distributionplot), 9E (statistical distribution plot) and 9F (statisticaldistribution plot) describe an application of a Monte Carlo simulationof the requirement, compliance and resource management methodology 140,as described in FIG. 9A. For example, FIG. 9C describes an optimum valuedistribution of a project, as an output of a Monte Carlo simulation.FIG. 9D describes a 5-year growth distribution, as an input to a MonteCarlo simulation. FIG. 9E describes a nominal tax distribution, as aninput to a Monte Carlo simulation. FIG. 9F describes a sales andgeneral/administrative expense (S&GA) distribution, as an input to aMonte Carlo simulation.

FIGS. 9G, 9H and 9I describe an embodiment of the weighting logic moduleof the requirement, compliance and resource management methodology 140,as described in FIG. 9A. For example, FIG. 9G describes a scaled totalimportance for an event (considering system, segment, element andassembly operations). FIG. 9H describes a scaled fraction for an event(considering system, segment, element and assembly operations). FIG. 9Idescribes a scaled % factor for an event (considering system, segment,element and assembly operations).

Tables 10A, 10B, 10C, 10D, 10E and 10F describe the features andbenefits of the requirement, compliance and resource managementmethodology 140, as described in FIG. 9A. Features and benefits Table10A describes specific features and benefits of 100A of the requirement,compliance and resource management methodology 140 in FIG. 9A. Featuresand benefits Table 10B describes specific features and benefits of 100A,100B, 100C and 100D of the requirement, compliance and resourcemanagement methodology 140 in FIG. 9A. Features and benefits Table 10Cdescribes specific features and benefits of 100D, 100E and 100F of therequirement, compliance and resource management methodology 140 in FIG.9A. Features and benefits Table 10D describes specific features andbenefits of 100F of the requirement, compliance and resource managementmethodology 140 in FIG. 9A. Features and benefits Table 10E describesspecific features and benefits of 100F, 100A1 and 100C1 of therequirement, compliance and resource management methodology 140 in FIG.9A. Features and benefits Table 10F describes specific features andbenefits of 100F1, 100F2 and 100F3 of the requirement, compliance andresource management methodology 140 in FIG. 9A

FIGS. 11A (schematic chart), 11B (schematic chart), 11C (schematicchart), 11D (schematic chart), 11E (schematic chart), 11F (schematicchart) and 11G (schematic chart) describe details of a typical processimplementation. FIG. 11A describes an overview of a typical processimplementation. FIG. 11B describes a granular view of a typical processimplementation, connecting with FIG. 11A. FIG. 11C describes a granularview of a typical process implementation, connecting with FIG. 11B. FIG.11D describes a granular view of a typical process implementation,connecting with FIGS. 11C and 11E (wherein FIG. 11E consists of FIG.11E1 and FIG. 11E2). FIG. 11E1 describes simulator specification of anexample subsystem 1. FIG. 11E2 describes simulator specification of anexample subsystem 2. FIG. 11F describes an example integrated masterschedule. FIG. 11G describes how a section of the integrated masterschedule (e.g., a requirement verification schedule) compares with totalprocess steps, verified process steps and planned process steps.

FIGS. 12A and 12B describe a process flowchart for a requirementspecification within a project setup. FIG. 12B is continuation of FIG.12A.

FIG. 13 describes a process flowchart for a requirement of aparent/child (also known as master/slave) relationship within a projectsetup.

FIG. 14 describes a process flowchart for a requirement category withina project setup.

FIG. 15 describes a process flowchart for a requirement verificationevent within a project setup.

FIG. 16 describes a process flowchart for a resource allocation processwithin a project setup,

FIG. 17A describes requirements, schedules, resources and personnelbefore the machine transformation.

FIG. 17B describes risk management, pending changes, deviation andwaiver (“dev & waiv”), giver/receiver and verification.

FIGS. 18A and 18B describe the machine transformation of requirements.FIG. 18B is the continuation of FIG. 18A.

FIG. 19 describes the machine transformation of schedules.

FIGS. 20A and 20B describe the machine transformation of resources. FIG.20B is the continuation of FIG. 20A.

FIG. 21 describes the machine transformation of personnel.

FIG. 22 describes the machine transformation, denoted as 5 a (5 a as inFIG. 17A). 5 a denotes the first machine transformation of theverification event.

FIG. 23 describes the machine transformation, denoted as 5 b (5 b as inFIG. 17A). 5 b denotes the second machine transformation of theverification event.

FIG. 24 describes the machine transformation, denoted as 5 c (5 c as inFIG. 17A). 5 c denotes the third machine transformation of theverification event.

FIG. 25A describes module 3160 (3160 as in FIG. 17A). Furthermore,module 3160 has cells identified as A, B, C, D, E, F, G, H, I and J.

FIG. 25B describes cell A of module 3160. FIG. 25C describes cell B ofmodule 3160. FIG. 25D describes cell C of module 3160. FIG. 25Edescribes cell D of module 3160. FIG. 25F describes cell E of module3160. FIG. 25G describes cell F of module 3160. FIG. 25H describes cellG of module 3160. FIG. 25I describes cell H of module 3160. FIG. 25Jdescribes cell I of module 3160. FIG. 25K describes cell J of module3160.

FIG. 26A describes requirements, schedules, resources and personnelbefore the machine transformation.

FIG. 26B describes risk management, pending changes, deviation andwaiver (“dev & waiv”), giver/receiver and verification.

FIG. 26C describes the machine transformation, denoted as 6 a (6 a as inFIG. 26A) 6 a denotes the first machine transformation of theverification event.

FIG. 26D describes the machine transformation, denoted as 6 b (6 b as inFIG. 26A). 6 b denotes the second machine transformation of theverification event.

FIG. 26E describes the module 3340 (3340 as in FIG. 26A).

FIG. 27A describes requirements, schedules, resources and personnelbefore the machine transformation.

FIG. 27B describes risk management, pending changes, deviation andwaiver (“dev & waiv”), giver/receiver and verification.

FIG. 27C describes the machine transformation, denoted as 7 a (7 a as inFIG. 27A). 7 a denotes the first machine transformation of theverification event.

FIG. 27D describes the machine transformation, denoted as 7 b (7 b as inFIG. 27A). 7 b denotes the second machine transformation of theverification event.

FIG. 27E describes the module 3520 (3520 as in FIG. 27A).

FIG. 28A describes requirements, schedules, resources and personnelbefore the machine transformation.

FIG. 28B describes risk management, pending changes, deviation andwaiver (“dev & waiv”), giver/receiver and verification.

FIG. 28C describes the machine transformation, denoted as 8 a (8 a as inFIG. 28A). 8 a denotes the first machine transformation of theverification event.

FIG. 28D describes the machine transformation, denoted as 8 b (8 b as inFIG. 28A). 8 b denotes the second machine transformation of theverification event.

FIG. 28E describes the module 3700 (3700 as in FIG. 28A).

FIG. 29A describes requirements, schedules, resources and personnelbefore the machine transformation.

FIG. 29B describes risk management, pending changes, deviation andwaiver (“dev & waiv”), giver/receiver and verification.

FIG. 29C describes the machine transformation, denoted as 9 a (9 a as inFIG. 29A). 9 a denotes the first machine transformation of theverification event.

FIG. 29D describes the machine transformation, denoted as 9 b (9 b as inFIG. 29A). 9 b denotes the second machine transformation of theverification event.

FIG. 29E describes the module 3880 (3800 as in FIG. 29A).

FIGS. 30A, 30B, 30C and 30D describe an example to establish a flowchartfor the module 3880. FIG. 30B is continuation of FIG. 30A. FIG. 30C iscontinuation of FIG. 30B.

FIG. 30D is continuation of FIG. 30C.

FIG. 31A describes requirements, schedules, resources and personnelbefore the machine transformation.

FIG. 31B describes risk management, pending changes, deviation andwaiver (“dev & waiv”), giver/receiver and verification.

FIG. 31C describes the machine transformation, denoted as 10 a (10 a asin FIG. 31A). 10 a denotes the first machine transformation of theverification event.

FIG. 31D describes the machine transformation, denoted as 10 b (10 b asin FIG. 31A). 10 b denotes the second machine transformation of theverification event.

FIG. 31E describes the graphical output of the module 4300 (4300 as inFIG. 31A).

FIGS. 32A and 32B describe an example to establish a flowchart for themodule 4300.

FIG. 32B is continuation of FIG. 32A.

FIG. 33A describes requirements, schedules, resources and personnelbefore the machine transformation.

FIG. 33B describes risk management, pending changes, deviation andwaiver (“dev & waiv”), giver/receiver and verification.

FIG. 33C describes the machine transformation, denoted as 11 a (11 a asin FIG. 33A). 11 a denotes the first machine transformation of theverification event.

FIG. 33D describes the machine transformation, denoted as 11 b (11 b asin FIG. 33A). 11 b denotes the second machine transformation of theverification event.

FIG. 33E describes the graphical output of the module 4620 (4620 as inFIG. 33A).

FIG. 34A describes memristors in a two-dimensional configuration.

FIG. 34B describes system on chip of memristors and hardware processorsin a three-dimensional configuration.

DETAIL DESCRIPTION OF THE PREFERRED EMBODIMENT OF THE INVENTION

FIG. 1 (schematic diagram) describes the various applications of therequirement, compliance and resource management methodology 100 (asdescribed in FIG. 5A) or 120 (as described in FIG. 7A) or 140 (asdescribed in FIG. 9A) in many industries (e.g., manufacturing,agriculture, pharmaceuticals, healthcare, energy, aerospace, defense andfinance (including banking)).

Furthermore, the requirement, compliance and resource managementmethodology 100 or 120 or 140 can be customized to fit anyproduct/service in any industry.

The requirement, compliance and resource management methodology 100 (asdescribed in FIG. 5A) configured/enhanced with the question/answerformat of a requirement input module and the fuzzy logic algorithmmodule can be designated as the requirement, compliance and resourcemanagement methodology 120 (as described in FIG. 7A).

Fuzzy means not clear (blurred). Fuzzy logic is a form of approximatereasoning, that can represent variation or imprecision in logic bymaking use of natural language (NL) in logic.

Approximation is inherent and inevitable in any process step andapproximation can be modeled and managed explicitly. A fuzzy logicalgorithm module can represent approximations for inputs and outputs inthe requirement, compliance and resource management methodology 120.

The requirement, compliance and resource management methodology 120 (asdescribed in FIG. 7A) further configured/enhanced with a statisticalalgorithm module and a weighting logic algorithm module can bedesignated as the requirement, compliance and resource managementmethodology 140 (as described in FIG. 9A).

Uncertainty/variation is inherent and inevitable in any process step anduncertainty/variation can be modeled and managed explicitly. Astatistical algorithm module can represent uncertainty/variation forinputs and outputs in the requirement, compliance and resourcemanagement methodology 140.

The requirement, compliance and resource management methodology 100 or120 or 140 can be integrated with an enterprise storage system (e.g., anenterprise server) and/or an enterprise device (e.g., a laptop and amobile internet appliance).

Alternatively, the requirement, compliance and resource managementmethodology 100 or 120 or 140 can be located at a cloud storage systemfor software-as-a service (SaaS).

Furthermore, the requirement, compliance and resource managementmethodology 100 or 120 or 140 is scalable.

Many components of the requirement, compliance and resource managementmethodology 100 or 120 or 140 are modular to permit automating somefunctions, but not automating other functions.

Furthermore, the components of the requirement, compliance and resourcemanagement methodology 100 or 120 or 140 can include (a) transactionaldatabase, (b) management portal/dashboard, (c) business intelligencesystem, (d) customizable reporting, (e) external access via internet,(f) search, (g) document management, (h) messaging/chat and (i) workflowmanagement.

Best practices can be incorporated in the requirement, compliance andresource management methodology 100 or 120 or 140. This means that therequirement, compliance and resource management methodology 100 or 120or 140 can reflect a defined interpretation as the most effective way toperform a process step and a customer can also modify the bestpractices.

Furthermore, the requirement, compliance and resource managementmethodology 100 or 120 or 140 can be configured with an applicationprogramming interface (API) to integrate (e.g., direct integrationand/or database integration) with other software programs (e.g., MSWord, MS Excel, MS Project and Enterprise Resource Planning (ERP)).

Enterprise Resource Planning (ERP) is an integrated softwareprogram/system that operates in near real time and/or real time, withoutrelying on periodic updates with a common database, which supports (a)finance/accounting (general ledger, payables, cash management, fixedassets, receivables, budgeting and consolidation), (b) human resources(payroll, training, benefits, 401K, recruiting and diversitymanagement), (c) manufacturing (bill of materials, engineering, workorders, scheduling, capacity, workflow management, quality control, costmanagement, manufacturing process, manufacturing projects, manufacturingflow, activity based costing and product life cycle management), (d)supply chain management (order to cash, inventory, order entry,purchasing, product configurator, supply chain planning, supplierscheduling, inspection of goods, claim processing and commissions), (e)project management (costing, billing, time and expense, performanceunits and activity management) and (f) customer relationship management(sales and marketing, commissions, service, customer contact and callcenter support).

FIG. 2 (schematic diagram) describes two-way connection of therequirement, compliance and resource management methodology 100 or 120or 140 (located at an enterprise storage system) to many systems (e.g.,work station) and/or devices (e.g., personal computer, laptop andinternet appliance). The internet appliance can be a mobile internetappliance (e.g., iPad).

FIG. 2 (schematic diagram) also describes one-way connection of therequirement, compliance and resource management methodology 100 or 120or 140 (located at an enterprise storage system) to a mobile phone. Theone-way connection can illustrate only summary result (summary dashboard) with a mobile phone, due to a limitation of the available displayscreen size.

FIG. 3 (schematic diagram) describes two-way connection of therequirement, compliance and resource management methodology 100 or 120or 140 (located at a cloud storage system) to many systems (e.g., workstation) and/or devices (e.g., personal computer, laptop and internetappliance). The internet appliance can be a mobile internet appliance(e.g., iPad).

FIG. 3 (schematic diagram) also describes one-way connection of therequirement, compliance and resource management methodology 100 or 120or 140 (located at a cloud storage system) to a mobile phone. Theone-way connection can illustrate only summary result (summary dashboard) with a mobile phone, due to a limitation of the available displayscreen size.

FIG. 4 (schematic diagram) describes two-way connection of therequirement, compliance and resource management methodology 100 or 120or 140 to various functional modules. User is denoted by 160, AlgorithmEngineering is denoted by 180, Hardware Engineering is denoted by 200,System Engineering is denoted by 220, Subcontracting is denoted by 240,Procurement is denoted by 260, Product Management is denoted by 280,Product Marketing is denoted by 300, Technical Support is denoted by320, Internal Legal is denoted by 340, External Legal (Compliance) isdenoted by 360, Financial Management is denoted by 380 and Executive(General) Management is denoted by 400.

FIG. 5A (block diagram) describes the requirement, compliance andresource management methodology 100 and all relevant modules aredescribed below: Requirement Processing Module is denoted by 100A,Compliance & Legal Module is denoted by 100B, Requirement Input Moduleis denoted by 100C, Specifications and Matrices Module is denoted by100D, Resource Allocation Module is denoted by 100E, Even VerificationModule is denoted by 100F and Graphical User Interface Module is denotedby 100A1.

Event verification module 100F can be configured with an applicationprogramming interface (API) to integrate (e.g., direct integrationand/or database integration) the requirement, compliance and resourcemanagement methodology 100 with other software programs (e.g., MS Word,MS Excel, MS Project and Enterprise Resource Planning (ERP)).

Graphical user interface module 100A1 can be configured a searchinterface for input data, interpretation of input data, analysis, outputdata and interpretation of output data.

The requirement processing module 100A can include an embeddedconstraint analysis tool. It adopts the common idiom that a chain is nostronger than its weakest link.

Assuming the goal of a project utilizing the requirement, compliance andresource management methodology and its success/failure measurements areclearly defined, then the process steps of the embedded constraintanalysis tool are:

-   -   1. identifying all constraints    -   2. deciding to exploit the constraints (how to get the most out        of the constraints)    -   3. making changes needed to break the first critical constraint    -   4. If the first critical constraint has been broken, then to go        to step 3 in order to break the second critical constrain, the        third critical constrain and so on.

Buffer can be used to protect the constraint from varying in the entirethe requirement, compliance and resource management methodology. Buffercan also allow for normal variation and the occasional upset before andbehind the constraint.

FIG. 5B1 and FIG. 5B2 are divided part of FIG. 5B. FIG. 5C1 and FIG. 5C2are divided part of FIG. 5C. FIG. 5E1 and FIG. 5E2 are divided part ofFIG. 5E. FIG. 5F1 and FIG. 5F2 are divided part of FIG. 5F. FIGS. 5B(schematic chart), 5C (schematic chart), 5D (schematic chart), 5E(schematic chart) and 5F (schematic chart) describe some typical outputsof some components of the embodiment of the requirement, compliance andresource management methodology 100 (as described in FIG. 5A).

An event coordination matrix (ECM) is a tool that can enablecross-functional and cross-enterprise coordination for facilitatingverification, validation, certification and accreditation (VVC&A)planning and execution.

The development of the ECM can be driving factor in verificationplanning activities. Typically, ECM can be developed early in theverification planning process to drive an early adoption amongst keystakeholders and also to allow for an identification of potentialdiscrepancies as early as possible.

The responsibility of the development of the ECM primarily relies oninputs from a test and verification (T&V) team, a system engineering(SE) team and an enterprise integration (EI) team, with additionalinputs provided by specialty engineering, quality assurance/missionassurance, information assurance and logistics planning.

The development of the ECM is a cross-enterprise activity and iscomprised of a four-part process:

-   -   1. identification of requirements,    -   2. identification of analysis, inspection, demonstration and        test (AIDT) events,    -   3. allocation of requirements to specific events, and    -   4. allocation of events to timelines or key events within        schedules.

The development, population and refinement of the ECM is coordinatedboth within the system engineering & integration (SE&I) organization andprime contractor organization by the EI team to ensure a thorough andbalanced approach across the enterprise.

Once all requirements (both imposed and derived) have been addressedthrough VVC&A and identified by the SE team, then all activities orevents where the VVC&A will occur have been identified by the T&V team,the requirements are then allocated to the set of specific events.

As depicted in FIG. 5F, the left side of the ECM includes therequirements information and the top of the ECM addresses the individualevents that are planned to accomplish the VVC&A.

Within the ECM, all activities and events (where VVC&A to be performed)are documented and tracked. The objective of the ECM is to correlate allrequirements to specific activities and events. By focusing on all VVC&Aactivities (as opposed to test only), it becomes possible to optimizethe T&V approach across the entire breadth of the program, allowing theT&V team to factor in analysis, inspection and demonstration events intotheir verification planning. By analyzing the VVC&A activities acrossthe program, the T&V team can act in a truly integrated fashion,optimizing the development and re-use of test data, scenarios, runconditions, truth models, environmental conditions and even theexecution of entire events to allow for efficient planning.

By looking at the complete picture of all integrated verificationactivities, the SE&I organization truly has insight and oversight intothe planned activities of the prime contractors and can identify arcasof the program, where there is either not enough verification beingplanned (for example, mission critical requirements (MCRs),interoperability requirements and critical technical parameter (CTP)requirements) or too much verification being planned (redundant orextraneous events).

An added benefit of this integrated approach to verification planning isthat it now becomes possible for the T&V organization to reportconfidence to the customer about when technical functionality will comeon-board and also to understand the impact of changes to schedule,performance and budget, thereby facilitating more accurate tradeanalysis and higher confidence recommendations on how to solve bothprogrammatic and technical problems as they arise.

A key consideration to note is the time-phase approach to theidentification of Analysis, Inspection, Demonstration & Test (AIDT)events. Identifying events that only represent final acceptance tests(FAT) as the primary focus of an integrated T&V approach isshort-sighted and will not allow the SE&I to truly act as a systemintegrator, thereby making it much more difficult to report incrementalprogress (and thus confidence) to the customer. As the programprogresses, the SE&I organization has identified analysis events thatwill occur prior to FAT. These analysis events allow the SE&Iorganization to analyze the technical details of the prime contractor'sexercises, rehearsals and even internal verification activities.

By scheduling analysis events that are centered on both technicalcapability delivery and reasonable time-phasing, the SE&I organizationcan more accurately predict when technical capabilities will bedelivered and provide more accurate, actionable data upon which thecustomer can make decisions.

Another key consideration is the design versus acceptance verification.The design verification encompasses those things typically performedonce for a system (induced environments, etc.) and, in many cases, byinspection. The acceptance verification can occur on acomponent-by-component or build-by-build basis. As the requirements areallocated to the events, the verification type (AIDT) is captured in theECM to ensure that the validation and verification is addressedadequately.

Given the considerations defined above, in order to optimize the benefitof a truly integrated SE&I methodology, all aspects of VVC&A have to beaddressed in one matrix ensuring the AIDT and VVC&A activities can beperformed once and at the lowest cost, risk and most optimum time/venue.

Tables 6A, 6B, 6C, 6D and 6E describe the features and benefits of therequirement, compliance and resource management methodology 100, asdescribed in FIG. 5A.

The key features and benefits of the requirement, compliance andresource management methodology 100 are listed below:

Requirement Processing Module (100A) Feature: Specification author “bookboss” assignments. Requirement Processing Module (100A) Benefit:Provides ability to assign personnel with read/write access tospecifications and requirements.

Compliance & Legal Module (100B) Feature: Import legal/regularityrequirements (i.e., HIPAA). Compliance & Legal Module (100B) Benefit:Single source for legal/regulatory requirement in a true relationaldatabase.

Requirement Input Module (100C) Feature (1): Import customerrequirements from MS Word/MS Excel/pdf into database. Requirement InputModule (100C) Benefit (1): Seamless import allows users to consolidaterequirements into single, true relational database. Requirement InputModule (100C) Feature (2): Incorporates non-textual objects and imagesinto database. Requirement Input Module (100C) Benefit (2): Allowsnon-textual objects to be associated with requirements objects.

Specifications and Matrices Module (100D) Feature (1): TPM, risk,critical issue tracking and control. Specifications and Matrices Module(100D) Benefit (1): Insightful reporting capability provides visibilityto critical issues and unresolved actions, enabling efficient resourceallocation. Specifications and Matrices Module (100D) Feature (2):Overall project completion status. Specifications and Matrices Module(100D) Benefit (2): Simple dashboard metrics which provide completionstatus at all levels of integration up to final end-item delivery.Specifications and Matrices Module (100D) Feature (3): Open actionstatus. Specifications and Matrices Module (100D) Benefit (3): Quick andeasy access to program action items and completion status.Specifications and Matrices Module (100D) Feature (4): Program usagestatistics. Specifications and Matrices Module (100D) Benefit (4):Real-time metrics which display iris user statistics such as userfrequency and duration.

Resource Allocation Module (100E) Feature (1): Hardware/softwareresource management. Resource Allocation Module (100E) Benefit (1):Allows for quick and easy reservation of hardware/software componentsneeded to perform verification activities in specificfacilities/locations. Flags, if there is a scheduling conflict inhardware/software resource allocation. Provides resource time and costfor each event. Resource Allocation Module (100E) Feature (2): Personnelresource management. Resource Allocation Module (100E) Benefit (2):Allows for quick and easy reservation of personnel and subject matterexperts needed to perform verification activities in specificfacilities/locations. Flags, if there is a scheduling conflict inhardware/software resource allocation. Provides resource time and costfor each event.

Event Verification Module (100F) Feature (1): Allocation of requirementsto verification events. Event Verification Module (100F) Benefit (1):Provides real-time visibility to verification strategies, configurationand objectives thereby providing programs the ability to leverageverification activities in support of agile acquisition initiatives.Enables collaboration ensuring early identification of risks. EventVerification Module (100F) Feature (2): Customizable verification eventcoordination matrix. Event Verification Module (100F) Benefit (2):Customizable event coordination matrix (ECM) generator which allowsusers to organize and group events by end-item deliverables andengineering disciplines. Provides ability for users to see if they canmove requirements to another event and the event in question may alsoeliminated thereby streamlining verification activities. EventVerification Module (100F) Feature (3): Event resource management. EventVerification Module (100F) Benefit (3): Tightly couples requiredverification event resources to integrated schedules to bettercoordinate resources. Event Verification Module (100F) Feature (4):Event configuration control and change history. Event VerificationModule (100F) Benefit (4): Ensures verification baseline is under strictconfiguration control. Maintains a detailed history of all changesagainst specific verification activities. Event Verification Module(100F) Feature (5): Traceability from requirements to compliance dataartifacts. Event Verification Module (100F) Benefit (5): Providesclosed-loop automated hyperlinks which provide quick access torequirements compliance data and related artifacts. Event VerificationModule (100F) Feature (6): Verification activity linkage to MS projectschedules. Event Verification Module (100F) Benefit (6): Tightly coupleswith verification activities with program milestones to ensure timelyend-item delivery. Event Verification Module (100F) Feature (7):Electronic signature (event planning and completion). Event VerificationModule (100F) Benefit (7): Electronic signature capability dramaticallyreduces test activity approval cycle. Event Verification Module (100F)Feature (8): Enterprise integration with external data sources. EventVerification Module (100F) Benefit (8): Allows for correlation of dataelements across the enterprise dramatically improving collaboration,increasing work force efficiency and reducing cost.

Graphical User Interface Module (100A1) Feature (1): Simple andintuitive GUI user interface. Graphical User Interface Module (100A1)Benefit (1): Simple, intuitive interface provides powerful capabilitiesfor importing, linking, analyzing, reporting and managing requirements,including traceability to associated project verification events andteam assignments. Requires minimal user training. Graphical UserInterface Module (100A1) Feature (2): Ready for use upon installation.Graphical User Interface Module (100A1) Benefit (2): No custom scriptingrequired results in lower implementation cost, faster usage. May betailored to support specific project processes.

A major challenge in the requirement, compliance and resource managementmethodology 100 (as described in FIG. 5A) is in qualitative andimprecise terms.

The use of soft functional requirements in a task-based specificationmethodology can capture the imprecise requirements and formulate softfunctional requirements using a fuzzy logic algorithm module. Morespecifically, the soft functional requirements can be represented bycanonical form in test-score semantics.

FIG. 7A (block diagram) describes another embodiment of the requirement,compliance and resource management methodology, further enhanced by aquestion and answer format of a requirement input module 100 C1 and afuzzy logic algorithm module 100F1 and all relevant modules aredescribed below: Requirement Processing Module is denoted by 100A,Compliance & Legal Module is denoted by 100B, Requirement Input Moduleis denoted by 100C, Specifications and Matrices Module is denoted by100D, Resource Allocation Module is denoted by 100E, Event VerificationModule is denoted by 100F, Graphical User Interface Module is denoted by100A1, Question & Answer Format For Requirement Input Module is denotedby 100C1 and Fuzzy Logic Algorithm Module is denoted by 100F1.

FIGS. 7B (schematic diagram) and 7C (schematic diagram) describes theimplementation of a fuzzy logic algorithm module 100F1.

A fuzzy logic algorithm module can be implemented as follows: (a) definelinguistic variables and terms, (b) construct membership functions, (c)construct rule base, (d) convert crisp inputs into fuzzy values,utilizing membership functions (fuzzification), (e) evaluate rules inthe rule base (inference), (f) combine the results of each rules(inference) and (g) convert outputs into non-fuzzy values(de-fuzzification).

Fuzzy logic is a relatively new technique for solving problems relatedto requirement, compliance and resource management methodology. The keyidea of fuzzy logic is that it uses a simple/easy way to secure theoutput(s) from the input(s), wherein the outputs can be related to theinputs by using if-statements.

Effective management of requirement, compliance and resource managementmethodology is crucial in producing a new product and/or new system.

In a competitive world, organizations are forced to look for scientifictools in evaluation of effective management of requirement, complianceand resource management methodology. The management team is responsiblefor producing an output and hence the management team must be constantlyaware of the goal, purpose and management efficiency. Furthermore,effectiveness in requirement, compliance and resource managementmethodology, which is a synonym of a project success, is measured orassessed in terms of the degree of achievement of project objectives.

For example, if project time delay (PTD) is low (L) and project timedelay gradient (PTDG) is high (H), then according to a fuzzy decision,the project management efficiency (PME) is very high (VH).

However, the boundaries of very high, high, medium and low of anydecision variable are determined by expert knowledge.

A fuzzy decision making system is a scientific tool that can be used tosolve the problem. This means that information of expert knowledge andexperience in a fuzzy decision making system is used for determining theproject management efficiency.

The development of such a fuzzy decision making system can beimplemented by utilizing the Mathworks software. Fuzzy Logic Toolboxfrom Mathworks Software is a menu driven software that can allow theimplementation of fuzzy constructs like membership functions and adatabase of decision rules.

Fuzzy Logic Toolbox from Mathworks Software also provides MathworksSoftware's MATLAB functions, graphical tools and Mathworks Software'sSimulink blocks for analyzing, designing and simulating systems based onfuzzy logic.

Furthermore, Fuzzy Logic Toolbox from Mathworks Software enables (a)design fuzzy inference systems, including fuzzy clustering andneuro-fuzzy system.

A neural network can approximate a function, but it is impossible tointerpret the result in terms of natural language. The fusion of neuralnetworks and fuzzy logic in neuro-fuzzy system can provide both learningas well as readability. Neuro-fuzzy system is based on combinations ofartificial neural networks and fuzzy logic.

Neuro-fuzzy system can use fuzzy inference engine with fuzzy rules formodeling the project uncertainties which is enhanced through learningthe various situations with a radial basis function (RBF) neuralnetwork.

Additionally, a neural network can approximate a function, but it isimpossible to interpret the result in terms of a natural language. Butan integration of the neural network and fuzzy logic in a neuro-fuzzyalgorithm can provide both learning and readability. The neuro-fuzzyalgorithm can use fuzzy inference engine (with fuzzy rules) for modelinguncertainties, which is further enhanced through learning the varioussituations with a radial basis function. The radial basis functionconsists of an input layer, a hidden layer and an output layer with anactivation function of hidden units. A normalized radial basis functionwith unequal widths and equal heights can be written as:

${{\psi_{i}(x)}({softmax})} = \frac{\exp\left( h_{i} \right)}{\sum_{i = 1}^{n}{\exp\left( h_{i\;} \right)}}$$h_{i} = \left( {- {\sum\limits_{l = 1}^{2}\frac{\left( {X_{l} - u_{il}} \right)^{2}}{2\sigma_{i}^{2}}}} \right)$X is the input vector, uil is the center of the ith hidden node (i=1, .. . , 12) that is associated with the lth (l=1, 2) input vector, σi is acommon width of the ith hidden node in the layer and softmax (hi) is theoutput vector of the ith hidden node. The radial basis activationfunction is the softmax activation function. First, the input data isused to determine the centers and the widths of the basis functions foreach hidden node. Second, is a procedure to find the output layerweights that minimize a quadratic error between predicted values andtarget values. Mean square error can be defined as:

${MSE} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}\left( {({TE})_{k}^{{ex}\; p} - ({TE})_{k}^{c\;{al}}} \right)^{2}}}$

For inherent uncertainties in the requirement, compliance and resourcemanagement methodology 120/140 due to external factors, shiftingbusiness objectives and poorly defined methods, a neuro-fuzzy system canbe utilized for scenario planning.

FIG. 7B describes crisp inputs are fed into fuzzifier module toinference module. Inference module is based on rules. The inferencemodule is fed into defuzzifier module then to crisp outputs.

FIG. 7C describes an application of fuzzy logic in a test design. Thetest design takes into account of (a) basic information, (b) customerspecial requirements, (c) knowledge rules and (d) mathematical modeling.Test design then creates a list of tests based fuzzy logic rules (fuzzylogic rules are based on graded performance database and weightingcoefficients) with ranking.

Fuzzy set theory is a generalization of the ordinary set theory. A fuzzyset is a set whose elements belong to the set with some degree ofmembership μ. Let X be a collection of objects. It is called universe ofdiscourse. A fuzzy set A

X is characterized by membership function μA(x) represents the degree ofmembership, Degree of membership maps each element between 0 and 1. Itis defined as: A={(x, μ_(A)(x)); x

X}.

FIG. 7D illustrates the membership functions of three fuzzy sets viz.“small”, medium” and “large” for a fuzzy variable X. The universe ofdiscourse is all possible values of Xs.

It is X=[15; 25]. At X of 18.75, the fuzzy set is a “small” withmembership value of 0.6. Hence, μ_(small) (18.75) is 0.6; μ_(medium)(18.75) is 0.4 and μ_(large) (18.75) is 0.4.

Fuzzy inference system is a rule-based system. It is based on fuzzy settheory and fuzzy logic. Fuzzy inference system is mappings from an inputspace to an output space. Fuzzy inference system allows constructingstructures which are used to generate responses (outputs) for certainstimulations (inputs). Response of fuzzy inference system is based onstored knowledge (relationships between responses and stimulations).Knowledge is stored in the form of a rule base. Rule base is a set ofrules. Rule base expresses relations between inputs of system and itsexpected outputs. Knowledge is obtained by eliciting information fromspecialists. These systems are usually known as fuzzy expert systems.Another common denomination for fuzzy inference system is fuzzyknowledge-based systems. It is also called as data-driven fuzzy systems.A fuzzy decision making system is comprised of four main components: afuzzification interface, a knowledge base, decision making logic, and adefuzzification interface. In essence, a fuzzy decision making system isa fuzzy expert system. A fuzzy expert system is oriented towardsnumerical processing where conventional expert systems are mainlysymbolic reasoning engines.

FIG. 7E describes a decision flow chart of the fuzzy logic module of therequirement, compliance and resource management methodology 120, asdescribed in FIG. 7A.

There are key four components in a decision flow chart of the fuzzylogic module: (a) The fuzzification interface: It measures the values ofthe input variables on their membership functions to determine thedegree of truth for each rule premise, (b) The knowledge base: Itcomprises experts' knowledge of the application domain and the decisionrules that govern the relationships between inputs and outputs. Themembership functions of inputs and outputs are designed by experts basedon their knowledge of the system and experience, (c) The decision-makinglogic: It is similar to simulating human decision making in inferringfuzzy control actions based on the rules of inference in fuzzy logic.The evaluation of a rule is based on computing the truth value of itspremise part and applying it to its conclusion part. This results inassigning one fuzzy subset to each output variable of the rule. In MinInference the entire strength of the rule is considered as the minimummembership value of the input variables' membership values. A rule issaid to be fire, if the degree of truth of the premise part of the ruleis not zero, (d) The defuzzification interface: It converts a fuzzycontrol action (a fuzzy output) into a nonfuzzy control action (a crispoutput). The most common used method in defuzzification is the center ofarea method (COA). The center of area method computes the crisp value asthe weighted average of a fuzzy set.

Tables 8A, 8B, 8C, 8D and 8E describe the features and benefits of therequirement, compliance and resource management methodology 120, asdescribed in FIG. 7A.

The key features and benefits of the requirement, compliance andresource management methodology 120 are listed below:

Requirement Processing Module (100A) Feature: Specification author “bookboss” assignments. Requirement Processing Module (100A) Benefit:Provides ability to assign personnel with read/write access tospecifications and requirements.

Compliance & Legal Module (100B) Feature: Import legal/regularityrequirements (i.e., HIPPA). Compliance & Legal Module (100B) Benefit:Single source for legal/regulatory requirement in a true relationaldatabase.

Requirement Input Module (100C) Feature (1): Import customerrequirements from MS Word/MS Excel/pdf into database. Requirement InputModule (100C) Benefit (1): Seamless import allows users to consolidaterequirements into single, true relational database. Requirement InputModule (100C) Feature (2): Incorporates non-textual objects and imagesinto database. Requirement Input Module (100C) Benefit (2): Allowsnon-textual objects to be associated with requirements objects.

Specifications and Matrices Module (100D) Feature (1): TPM, risk,critical issue tracking and control. Specifications and Matrices Module(100D) Benefit (1): Insightful reporting capability provides visibilityto critical issues and unresolved actions, enabling efficient resourceallocation. Specifications and Matrices Module (100D) Feature (2):Overall project completion status. Specifications and Matrices Module(100D) Benefit (2): Simple dashboard metrics which provide completionstatus at all levels of integration up to final end-item delivery.Specifications and Matrices Module (100D) Feature (3): Open actionstatus. Specifications and Matrices Module (100D) Benefit (3): Quick andeasy access to program action items and completion status.Specifications and Matrices Module (100D) Feature (4): Program usagestatistics. Specifications and Matrices Module (100D) Benefit (4):Real-time metrics which display iris user statistics such as userfrequency and duration.

Resource Allocation Module (100E) Feature (1): Hardware/softwareresource management. Resource Allocation Module (100E) Benefit (1):Allows for quick and easy reservation of hardware/software componentsneeded to perform verification activities in specificfacilities/locations. Flags, if there is a scheduling conflict inhardware/software resource allocation. Provides resource time and costfor each event. Resource Allocation Module (100E) Feature (2): Personnelresource management. Resource Allocation Module (100E) Benefit (2):Allows for quick and easy reservation of personnel and subject matterexperts needed to perform verification activities in specificfacilities/locations. Flags, if there is a scheduling conflict inhardware/software resource allocation. Provides resource time and costfor each event.

Event Verification Module (100F) Feature (1): Allocation of requirementsto verification events. Event Verification Module (100F) Benefit (1):Provides real-time visibility to verification strategies, configurationand objectives thereby providing programs the ability to leverageverification activities in support of agile acquisition initiatives.Enables collaboration ensuring early identification of risks. EventVerification Module (100F) Feature (2): Customizable verification eventcoordination matrix. Event Verification Module (100F) Benefit (2):Customizable event coordination matrix (ECM) generator which allowsusers to organize and group events by end-item deliverables andengineering disciplines. Provides ability for users to see if they canmove requirements to another event and the event in question may alsoeliminated thereby streamlining verification activities. EventVerification Module (100F) Feature (3): Event resource management. EventVerification Module (100F) Benefit (3): Tightly couples requiredverification event resources to integrated schedules to bettercoordinate resources. Event Verification Module (100F) Feature (4):Event configuration control and change history. Event VerificationModule (100F) Benefit (4): Ensures verification baseline is under strictconfiguration control. Maintains a detailed history of all changesagainst specific verification activities. Event Verification Module(100F) Feature (5): Traceability from requirements to compliance dataartifacts. Event Verification Module (100F) Benefit (5): Providesclosed-loop automated hyperlinks which provide quick access torequirements compliance data and related artifacts. Event VerificationModule (100F) Feature (6): Verification activity linkage to MS projectschedules. Event Verification Module (100F) Benefit (6): Tightly coupleswith verification activities with program milestones to ensure timelyend-item delivery. Event Verification Module (100F) Feature (7):Electronic signature (event planning and completion). Event VerificationModule (100F) Benefit (7): Electronic signature capability dramaticallyreduces test activity approval cycle. Event Verification Module (100F)Feature (8): Enterprise integration with external data sources. EventVerification Module (100F) Benefit (8): Allows for correlation of dataelements across the enterprise dramatically improving collaboration,increasing work force efficiency and reducing cost.

Graphical User Interface Module (100A1) Feature (1): Simple andintuitive GUI user interface. Graphical User Interface Module (100A1)Benefit (1): Simple, intuitive interface provides powerful capabilitiesfor importing, linking, analyzing, reporting and managing requirements,including traceability to associated project verification events andteam assignments. Requires minimal user training. Graphical UserInterface Module (100A1) Feature (2): Ready for use upon installation.Graphical User Interface Module (100A1) Benefit (2): No custom scriptingrequired results in lower implementation cost, faster usage. May betailored to support specific project processes.

Question & Answer Format For Requirement Input Module (100C1) Feature(1) Project setup question and answer. Question & Answer Format ForRequirement Input Module (100C1) Benefit (1): Step-by-step question andanswer that allows user to quickly and easily set up a new project.

Fuzzy Logic Algorithm Module 100F1 Feature (1): Verification completiondecision (fuzzy logic). Fuzzy Logic Algorithm Module 100F1 Benefit (1):Enables program decision makers to assess when verification is goodenough. Fuzzy Logic Algorithm Module 100F1 Feature (2): “Requirementgoodness” estimation (fuzzy logic). Fuzzy Logic Algorithm Module 100F1Benefit (2): Evaluates requirement goodness thereby reducing requirementrework and verification resource waste.

FIG. 9A (block diagram) describes another embodiment of the requirement,compliance and resource management methodology 140, further enhanced bya question and answer format of requirement input module 100C1, a fuzzylogic algorithm module 100F1, a statistical algorithm module 100F2 and aweighting logic algorithm module 100F3 and all relevant modules aredescribed below: Requirement Processing Module is denoted by 100A,Compliance & Legal Module is denoted by 100B, Requirement Input Moduleis denoted by 100C, Specifications and Matrices Module is denoted by100D, Resource Allocation Module is denoted by 100E, Event VerificationModule is denoted by 100F, Graphical User Interface Module is denoted by100A1, Question & Answer Format For Requirement Input Module is denotedby 100C1, Fuzzy Logic Algorithm Module is denoted by 100F1, StatisticalAlgorithm Module is denoted by 100F2 and Weighting Logic AlgorithmModule is denoted by 100F3.

FIG. 9B (schematic chart) describes the implementation result of astatistical algorithm module 100F2.

Statistical Algorithm Module (100F2) Feature (1): Statisticsvariability. Statistical Algorithm Module (100F2) Benefit (1): Providesstatistical estimating capability for empirical results that requirestatistical modeling to assess performance variability.

Furthermore, the statistical algorithm module (100F2) can be alsoconfigured with a Monte Carlo simulation.

A Monte Carlo simulation can help solve problems that are toocomplicated to solve using equations or problems for which no equationsexist. It is useful for problems which have lots of uncertainty ininputs.

In cost management, one can use Monte Carlo simulation to betterunderstand project budget and estimate final budget at completion.Instead of assigning a probability distribution to the project taskdurations, project manager assigns the distribution to the projectcosts. These estimates are normally produced by a project cost expert,and the final product is a probability distribution of the final totalproject cost. Project managers often use this distribution to set asidea project budget reserve, to be used when contingency plans arenecessary to respond to risk events. Monte Carlo simulation can also beused when making capital budgeting and investment decisions. Riskanalysis is part of every decision made in the requirement, complianceand resource management.

The requirement, compliance and resource management is constantly facedwith uncertainty, ambiguity and variability. And even though there maybe an unprecedented access to information, one can't accurately modelthe future.

A Monte Carlo simulation allows seeing all the possible outcomes ofdecisions and assessing the impact of risk, allowing for better decisionmaking under uncertainty for requirement, compliance and resourcemanagement.

A Monte Carlo simulation can be added utilizing add-ins such as @ Riskor Risk+ algorithm.

A Monte Carlo simulation encompasses a technique of statistical samplingto approximate a solution to a quantitative problem.

The requirement, compliance and resource management methodology containsmany variables. However, each variable has many possible valuesrepresented by a probability distribution function p(x).

Probability distribution function p(x) of each variable is a realisticway of describing uncertainty in each variable in a risk analysis.

By contrast, a Monte Carlo simulation can sample probabilitydistribution function for each variable to produce hundreds or thousandsof possible outcomes. The results are analyzed to get probabilities ofdifferent outcomes occurring.

In contrast to a Monte Carlo simulation, a spreadsheet project costmodel utilizes traditional “what if” scenarios, wherein “what if”analysis gives equal weight to all scenarios.

Common probability distribution functions p(x) are: Normal/“BellCurve”—The user simply defines the mean or expected value and a standarddeviation to describe the variation about the mean. Values in the middlenear the mean are most likely to occur. Lognormal—Values are positivelyskewed, not symmetric like a normal distribution. It is used torepresent values that don't go below zero but have unlimited positivepotential. Uniform—All values have an equal chance of occurring, and theuser simply defines the minimum and maximum. Triangular—The user definesthe minimum, most likely, and maximum values. Values around the mostlikely are more likely to occur. Variables that could be described by atriangular distribution include past sales history per unit of time andinventory levels. PERT—The user defines the minimum, most likely, andmaximum values, just like the triangular distribution. Values around themost likely are more likely to occur. However, values between the mostlikely and extremes are more likely to occur than the triangular; thatis, the extremes are not as emphasized. Discrete—The user definesspecific values that may occur and the likelihood of each.

A Monte Carlo simulation performs a risk analysis by building models ofpossible results by substituting a range of values—a probabilitydistribution p(x) for any variable/factor that has an inherentuncertainty. It then calculates results over and over, each time using adifferent set of random values from the probability function p(x).Depending on the number of uncertainties and the ranges specified forthem, a Monte Carlo simulation could involve thousands or tens ofthousands of recalculations before it is completed. A Monte Carlosimulation produces distributions of possible outcome values.

A Monte Carlo simulation simulates the requirement, compliance andresource management methodology many times (thousands or tens ofthousands of recalculations) and each time selecting a value of eachvariable from its probability distribution function p(x).

The outcome is a probability distribution of overall compliance andresource management methodology 140 through iterations of the model.

A Monte Carlo simulation is a powerful tool to quantify the potentialeffects of uncertainties of many variables in the requirement,compliance and resource management methodology 140.

But it should be noted a Monte carol simulation is only as good as modelit is simulating and data/information/probability distribution functionp(x) of a variable is fed into.

Furthermore, open-ended distributions (e.g., lognormal distribution) canbe preferable than closed-ended (e.g., triangular distribution)distributions in a Monte carol simulation.

A Monte Carlo simulation can generally answer to the questions e.g.,what is the probability of meeting the project budget? or what is theprobability of meeting the project time deadline? or what is an optimumvalue of a project cost?

A Monte Carlo simulation provides a number of advantages overdeterministic or “single-point estimate” analysis.

For example: Probabilistic Results. Results show not only what couldhappen, but how likely each outcome is.

For example: Graphical Results. Because of the data, a Monte Carlosimulation generates, it is easy to create graphs of different outcomesand their chances of occurrence. This is important for communicatingfindings to all stakeholders.

For example: Sensitivity Analysis. With just a few cases, deterministicanalysis makes it difficult to see which variables impact the outcomethe most. In a Monte Carlo simulation, it is easy to see which inputshad the biggest effect on bottom-line results.

For example: Scenario Analysis: In deterministic models, it is verydifficult to model different combinations of values for different inputsto see the effects of truly different scenarios. Using a Monte Carlosimulation, analysts can see exactly which inputs had which valuestogether when certain outcomes occurred. This is invaluable for pursuingfurther analysis.

For example: Correlation of Inputs. In a Monte Carlo simulation, it'spossible to model interdependent relationships between input variables.It's important for accuracy to represent how, in reality, when somefactors go up, others go up or down accordingly.

FIG. 9C (statistical distribution plot) describes an outcome/outputdistribution of a project cost based on a Monte Carlo simulation.

FIGS. 9D (statistical distribution plot), 9E (statistical distributionplot) and 9F (statistical distribution plot) are typical inputs of aMonte Carlo simulation.

FIGS. 9G (schematic chart), 9H (schematic chart) and 9I (schematicchart) describes an implementation of the weighting logic algorithm.

Top-level requirements are decomposed into lower level requirements in atree format as shown in FIG. 9G.

In FIG. 9G the weighting logic algorithm module 100F3 provides a methodof increasing confidence in the prediction of TPMs. Parametric valuesare vertically summed for each level of integration for a given system(i.e., System, Segment, Element and Assembly) and shown in the “SpecSum” row. An arbitrary numeric scaling factor or weight is applied toeach level of assembly, thereby increasing the influence that the summedvalue has on the overall system for that particular level ofintegration. Summed values are multiplied by respective scale factors toproduce a scaled total which is then added to yield an overallverification amount, 485 in this example. The system level parametricvalue of 15 is then divided by 485 to yield 0.0309, an effectivesystem-level scaling factor which can be applied to each measured valueof the overall system.

In FIG. 9H the system level scaling factor (0.0309) is multiplied byeach measured value in the “tree”, then multiplied by the Spec Scalefactor from FIG. 9C. To obtain the “Scaled Total” values, the systemlevel scaling factor (0.0309) is multiplies by the “Spec Sum” which isthen multiplied by the scale factor for each level of integration. Forexample, the “Scaled Total” value for the “Segment” level of integrationwould be: system level scaling factor (0.0309)*Spec Scale Factor(2)*“Spec Sum” (21)=1.30.

In FIG. 9I to obtain the percent total that each level of integration'sverification data contributes to the overall system-level TPM, the“Scaled Total” values from FIG. 9D is divided by the System-levelrequirement value (15). For example, the assembly level contributionwould be 9.40/15 or 62.7%.

The requirement, compliance and resource management methodology canprovide a method of predicting system performance parameters throughoutthe program development life cycle. As top-level system requirements ortechnical performance measurements (TPMs) are assessed, a statisticalweighting algorithm gives users the ability to weight or influence theempirical data of some elements more than others in the same set.

As measurements are collected to verify lower level requirements, therequirement, compliance and resource management methodology can provideusers with the ability to assign an arbitrary weighting coefficient tothese measurements to increase their influence on the top-levelperformance prediction at a given point in time.

Lower level measurement weighting coefficients are typically greaterthan higher level coefficients, since there are a fewer system elements(variables) associated with the lower level measurement, therebyincreasing measurement confidence.

Tables 10A, 10B, 10C, 10D, 10E and 10F describe the features/benefits ofthe requirement, compliance and resource management methodology 140, asdescribed in FIG. 9A.

The key features and benefits of the requirement, compliance andresource management methodology 140 are listed below:

Requirement Processing Module (100A) Feature: Specification author “bookboss” assignments. Requirement Processing Module (100A) Benefit:Provides ability to assign personnel with read/write access tospecifications and requirements.

Compliance & Legal Module (100B) Feature: Import legal/regularityrequirements (i.e., HIPPA). Compliance & Legal Module (100B) Benefit:Single source for legal/regulatory requirement in a true relationaldatabase.

Requirement Input Module (100C) Feature (1): Import customerrequirements from MS Word/MS Excel/pdf into database. Requirement InputModule (100C) Benefit (1); Seamless import allows users to consolidaterequirements into single, true relational database. Requirement InputModule (100C) Feature (2): Incorporates non-textual objects and imagesinto database. Requirement Input Module (100C) Benefit (2): Allowsnon-textual objects to be associated with requirements objects.

Specifications and Matrices Module (100D) Feature (1): TPM, risk,critical issue tracking and control. Specifications and Matrices Module(100D) Benefit (1): Insightful reporting capability provides visibilityto critical issues and unresolved actions, enabling efficient resourceallocation. Specifications and Matrices Module (100D) Feature (2):Overall project completion status. Specifications and Matrices Module(100D) Benefit (2): Simple dashboard metrics which provide completionstatus at all levels of integration up to final end-item delivery.Specifications and Matrices Module (100D) Feature (3): Open actionstatus. Specifications and Matrices Module (100D) Benefit (3): Quick andeasy access to program action items and completion status.Specifications and Matrices Module (100D) Feature (4): Program usagestatistics. Specifications and Matrices Module (100D) Benefit (4):Real-time metrics which display iris user statistics such as userfrequency and duration.

Resource Allocation Module (100E) Feature (1): Hardware/softwareresource management. Resource Allocation Module (100E) Benefit (1):Allows for quick and easy reservation of hardware/software componentsneeded to perform verification activities in specificfacilities/locations. Flags, if there is a scheduling conflict inhardware/software resource allocation. Provides resource time and costfor each event. Resource Allocation Module (100E) Feature (2): Personnelresource management. Resource Allocation Module (100E) Benefit (2):Allows for quick and easy reservation of personnel and subject matterexperts needed to perform verification activities in specificfacilities/locations. Flags, if there is a scheduling conflict inhardware/software resource allocation. Provides resource time and costfor each event.

Event Verification Module (100F) Feature (1): Allocation of requirementsto verification events. Event Verification Module (100F) Benefit (1):Provides real-time visibility to verification strategies, configurationand objectives thereby providing programs the ability to leverageverification activities in support of agile acquisition initiatives.Enables collaboration ensuring early identification of risks. EventVerification Module (100F) Feature (2): Customizable verification eventcoordination matrix. Event Verification Module (100F) Benefit (2):Customizable event coordination matrix (ECM) generator which allowsusers to organize and group events by end-item deliverables andengineering disciplines. Provides ability for users to see if they canmove requirements to another event and the event in question may alsoeliminated thereby streamlining verification activities. EventVerification Module (100F) Feature (3): Event resource management. EventVerification Module (100F) Benefit (3): Tightly couples requiredverification event resources to integrated schedules to bettercoordinate resources. Event Verification Module (100F) Feature (4):Event configuration control and change history. Event VerificationModule (100F) Benefit (4): Ensures verification baseline is under strictconfiguration control. Maintains a detailed history of all changesagainst specific verification activities. Event Verification Module(100F) Feature (5): Traceability from requirements to compliance dataartifacts. Event Verification Module (100F) Benefit (5): Providesclosed-loop automated hyperlinks which provide quick access torequirements compliance data and related artifacts. Event VerificationModule (100F) Feature (6): Verification activity linkage to MS projectschedules. Event Verification Module (100F) Benefit (6): Tightly coupleswith verification activities with program milestones to ensure timelyend-item delivery. Event Verification Module (100F) Feature (7):Electronic signature (event planning and completion). Event VerificationModule (100F) Benefit (7): Electronic signature capability dramaticallyreduces test activity approval cycle. Event Verification Module (100F)Feature (8): Enterprise integration with external data sources. EventVerification Module (100F) Benefit (8): Allows for correlation of dataelements across the enterprise dramatically improving collaboration,increasing work force efficiency and reducing cost.

Graphical User Interface Module (100A1) Feature (1): Simple andintuitive GUI user interface. Graphical User Interface Module (100A1)Benefit (1): Simple, intuitive interface provides powerful capabilitiesfor importing, linking, analyzing, reporting and managing requirements,including traceability to associated project verification events andteam assignments. Requires minimal user training. Graphical UserInterface Module (100A1) Feature (2): Ready for use upon installation.Graphical User Interface Module (100A1) Benefit (2): No custom scriptingrequired results in lower implementation cost, faster usage. May betailored to support specific project processes.

Question & Answer Format For Requirement Input Module (100C1) Feature(1) Project setup question and answer. Question & Answer Format ForRequirement Input Module (100C1) Benefit (1): Step-by-step question andanswer that allows user to quickly and easily set up a new project.

Fuzzy Logic Algorithm Module 100F1 Feature (1): Verification completiondecision (fuzzy logic). Fuzzy Logic Algorithm Module 100F1 Benefit (1):Enables program decision makers to assess when verification is goodenough. Fuzzy Logic Algorithm Module 100F1 Feature (2): “Requirementgoodness” estimation (fuzzy logic). Fuzzy Logic Algorithm Module 100F1Benefit (2): Evaluates requirement goodness thereby reducing requirementrework and verification resource waste.

Weighting Logic Algorithm Module (100F3) Feature (1): TPM calculator(weighting logic). Weighting Logic Algorithm Module (100F3) Benefit (1):Allows program to calculate value of TPM throughout integration process.

FIGS. 11A (schematic chart) and 11B (schematic chart), describespecification development of a process implementation.

FIG. 11C (schematic chart) describes a typical verification summarysheet of a process implementation.

FIG. 11D (schematic chart) describes interaction between summary sheetof a process implementation (as described in FIG. 11C), simulationplans, test plans, test procedures, data verification and data analysis(as described in FIG. 11D) and simulation specifications (as describedin FIG. 11E).

FIG. 11E (schematic chart) describes a typical simulation specificationof a process implementation.

FIG. 11F (schematic chart) describes a typical integrated masterschedule of a process implementation.

FIG. 11G (schematic chart) describes a requirement verification scheduleof a process implementation.

In FIGS. 11A-11B the development of the Event Coordination Sheets (ECS)starts with the baseline specifications. In section 4.0 of systemspecifications, verification methods are assigned to each requirement inaccordance with applicable standards. Requirements are then mapped intoverification events based on the event objectives. One approach todefining verification events and determining which requirements shouldbe mapped into specific verification events is to develop a spreadsheetsimilar to that shown in FIGS. 11A and 11B. TPMs and Mission Criticalrequirements are then identified. A balanced VSS approach will carefullyallocate requirements into appropriate venues such that redundantverification, or “double-booking”, is minimized.

In FIGS. 11C-11E once requirements have been allocated into verificationvenues, the ECS can now be created using the instructions below:

Description: A concise statement delineating the verification to beperformed. If the verification has more than one sequence, break thesequence out here. Describe relationships among verification methods(e.g., where test output will be used to perform an analysis). Ifverification activities have been completed, type “Refer to referencedreport(s).” If N/A, provide a brief explanation.

Objectives: Provide a concise overview of verification activityobjectives. If the verification activity is conducted in severalsequences, objectives may be written for each sequence, provided theyaddress the requirements

Success Criteria: Provide a brief description of verification activitypass/fail criteria. This must include the specific data and the resultsof any analyses that may be required to interpret the data and concludewhether or not the requirement has been successfully verified.

Requirements: (Include requirement paragraph and/or requirement ID.):Provide a comprehensive list of all the requirements that have beenallocated to a given verification activity.

Timeline/Schedule: Define the expected duration of the verificationactivity relative to program milestones. Includes the expected durationof the entire verification activity including verification activitypreparation, execution, data acquisition and data post processing anddata analysis.

Constraints: Identify limitations on the extent of the verificationactivity conducted. Identify any special conditions on the test setup,test article, environmental conditions etc.

Pre-Test Requirements: Identify any special test equipment or resources.Reference report number and title only. (Applies only if verificationprocedure has been completed and report written.) If not applicable(“N/A”), to provide a brief explanation.

Configuration: Identify the hardware or software configuration for useduring this verification procedure(s).

Data Acquisition Requirements: List verification procedure datarequirements and products. Reference report number and title only.(Applies only if verification procedure has been completed and reportwritten.).

Evidence of Closure: Identify the document title and number of thereferenced report that contains the data which verifies that this(these) requirement(s) have been met. Attach referenced material toverification event form.

Each event will be coordinated using the requirement, compliance andresource management methodology (100/120/140)′ dynamic schedule linkingcapability, which synchronizes events with the Integrated MasterSchedule as shown in FIGS. 11F and 11G.

FIGS. 12A and 12B describe a process flowchart for requirementspecification within a project setup.

In step 1020 one can create a user account, in step 1040 one can assignan access to a user and in step 1060 one can assign a level of access tothe user.

In step 1080 the user can create a requirement specification tree, instep 1100 the user can name a requirement specification document, instep 1120 the user can describe the requirement specification document,in step 1140 the user can create the requirement specification documentversion number, in step 1160 the user can assign an access to otherusers, regarding the requirement specification document with a specificversion, in step 1180 the user can create the requirement specificationdocument directly, or otherwise in step 1220 the user can import therequirement specification document utilizing MS Excel program. In step1240 if the imported requirement specification document is OK, then theuser can stop in step 1280; otherwise the user can review the integrityof the imported requirement specification document in step 1260.

FIG. 13 describes a process flowchart for a requirement of parent/child(also known as master/slave) relationship within a project setup.

In step 1300 the user can define a requirement of importing parent/childrelationship. In step 1320 the user can create the requirement ofparent/child relationship directly and if this direct creation of therequirement of parent/child relationship is successful, then the usercan stop in step 1340; otherwise, in step 1360 the user can import theparent/child relationship template by utilizing MS Excel program, instep 1380 the user can review the integrity of the imported parent/childrelationship template. In step 1400 the user can import a requirement ofparent/child relationship, in step 1420 the user can verify theintegrity of the imported requirement of parent/child relationshiputilizing a parent/child flow down report. In step 1440, if the importedrequirement of parent/child relationship is OK, then the user can stopin step 1460; otherwise the user can reiterate to step 1380.

FIG. 14 describes a process flowchart for a requirement category withina project setup.

In step 1480 the user can define a requirement category. In step 1500the user can create a requirement category directly. If the directcreation of the requirement category is successful, then the user canstop in step 1520; otherwise in step 1540 the user can import arequirement category template utilizing MS Excel program. In step 1560the user can review the integrity of the imported requirement categorytemplate, in step 1580 the user can import a requirement category and instep 1600 the user can verify the integrity of the imported requirementcategory utilizing category filters. In step 1620 if the importedrequirement category is OK, then the user can stop in step 1640;otherwise the user can reiterate to step 1560.

FIG. 15 describes process flowchart for a requirement verification eventwithin a project setup. A verification event is a generic activity usedto verify requirements by inspection, demonstration, analysis and test.

In step 1660 the user can define a requirement verification event withina project setup. In step 1680 the user can create a requirementverification event directly. If the direct creation of requirementverification event is successful, then the user can stop in step 1700;otherwise in step 1720 the user can import a requirement verificationevent template utilizing MS Excel program. In step 1740 the user canreview the integrity of the imported requirement verification eventtemplate, in step 1760 the user can import a requirement verificationevent, in step 1780 the user can verify the integrity of the importedrequirement verification event, utilizing a verification event report,in step 1800 if the imported requirement verification event is OK, thenthe user can stop in step 1820; otherwise the user can reiterate to step1740.

FIG. 16 describes process flowchart for a resource allocation processwithin a project setup.

In step 1840 the user can ask a question if there are required resourcesto execute the event, if the answer is no, then the user can stop instep 1860. However, if the answer to the above question is yes, then theuser can proceed to step 1880.

In step 1880 the user can ask a question if there are required softwareto execute the event, if the answer is no, then the user can proceed tostep 2000. However, if the answer to the above question is yes, then theuser can proceed to step 1900.

In step 1900 the user can input site location, where software will beused. In step 1920 the user can input lab/facility (within the sitelocation) where the software will be used. In step 1940 the user caninput required software component name and version. In step 1960 theuser can input software start date and end date.

If the answer to the question (is there specific hardware to execute theevent?) in step 2000 is yes, then the user can proceed to step 2040;otherwise the user can stop at 2020. In step 2040 the user can inputsite location, where hardware will be used. In step 2060 the user caninput lab/facility (within the site location) where the hardware will beused. In step 2080 the user can input required hardware component nameand version. In step 2100 the user can input hardware start date and enddate and stop is indicated as step 2120.

In FIG. 17A, requirements, schedules, resources and personnel areidentified as 2140, 2160, 2180 and 2200 respectively before the machinetransformation.

In FIG. 17A, requirements, schedules, resources and personnel areidentified as 2260, 2300, 2320 and 2340 respectively after the machinetransformation.

In FIG. 17A, action item, issue and verification events are identifiedas 2220, 2240 and 2280 respectively.

Furthermore, FIG. 17A, incorporates various machine transformations,which are denoted as 1, 2, 3, 4, 5 a, 5 b and 5 c.

Furthermore, in FIG. 17B, risk management, pending changes, deviationand waiver (“dev & waiv”), giver/receiver and verification results aredenoted by 2360, 2380, 2400, 2420 and 2440 respectively.

FIGS. 18A and 18B illustrate the machine transformation of requirementsdenoted as 1.

In FIG. 18A, in step 2460 purge requirements from data tables, in step2480 import requirements from web services, in step 2500 purgespecification names/versions from data tables, in step 2520 importspecification names/versions from web services.

In FIG. 18B, in step 2540 purge specification document phases, in step2560 import specification document phases from web services, in step2580 purge requirements from data tables and in step 2600 importrequirements from web services.

FIG. 19 illustrates the machine transformation of schedules denoted as2. In step 2620 purge event dates from tables, in step 2640 import eventdates from web services, in step 2660 purge event names from data tablesand in step 2680 import event names from web services.

FIGS. 20A and 20B illustrate the machine transformation of resourcesdenoted as 3.

In FIG. 20A, in step 2700 purge “facilities” field from data tables, instep 2720 import “facilities” field from web services, in step 2740purge “hardware” field from data tables and in step 2760 import“hardware” field from web services.

In FIG. 20B, in step 2780 purge “software” field from data tables, instep 2800 import “software” field from web services, in step 2820 purge“software” field from data tables and in step 2840 import “software”field from web services.

FIG. 21 illustrates the machine transformation of personnel and themachine transformation of personnel is denoted as 4.

In FIG. 21, in step 2860 purge “team” field from data tables and in step2880 import “team” field from web services.

FIG. 22 illustrates the machine transformation, denoted as 5 a. In FIG.22, in step 3000 list requirement parameter, ID, name and text, in step3020 list event ID, name, event developer and conductor and in step 3040correlate requirement numbers with event numbers.

FIG. 23 illustrates the machine transformation, denoted as 5 b. In FIG.23, in step 3060 calculate requirement allocations for each event, instep 3080 calculate number of times requirement is allocated to an eventand in step 3100 enables format/display matrix.

FIG. 24 illustrates the machine transformation, denoted as 5 c. In FIG.24, in step 3120 enables filter by specification and in step 3140enables format for export.

FIG. 25A illustrates module 3160 with cells identified as A, B, C, D, E,F, G, H, I and J. 3160 module is a matrix correlating verificationevents, as illustrated in A, B, C, event EIS developer/conductor (EventIntegration Sheet—EIS), as illustrated in D, E, F with specifiedrequirements and/or compliance attributes as illustrated in G.

FIGS. 25B, 25C, 25D, 25E, 25F, 25G, 25H, 25I, 25J and 25 K illustratecells A, B, C, D, E, F, G, H, I and J respectively for module 3160.

In FIG. 26A, requirements, schedules, resources and personnel areidentified as 2140, 2160, 2180 and 2200 respectively before the machinetransformation.

In FIG. 26A, requirements, schedules, resources and personnel areidentified as 2260, 2300, 2320 and 2340 respectively after the machinetransformation.

In FIG. 26A, action item, issue and verification events are identifiedas 2220, 2240 and 2280 respectively.

Furthermore, FIG. 26A, incorporates various machine transformations,which are denoted as 1, 2, 3, 4, 6 a and 6 b.

The machine transformations denoted as 1, 2, 3 and 4 have beenillustrated in previous paragraphs from 254 to 262.

In FIG. 26B, risk management, pending changes, dev & waiv,giver/receiver and verification results are denoted by 2360, 2380, 2400,2420 and 2440 respectively.

FIG. 26C illustrates the machine transformation, denoted as 6 a. In FIG.26C, in step 3180 populate/lab facility resource data base, in step 3200allocate lab/facility resources to events, in step 3220 select neededstart and end date and in step 3240 sort labs/facilities.

FIG. 26D illustrates the machine transformation, denoted as 6 b. In FIG.26D, in step 3260 identify labs/facilities where start/end datesoverlap, in step 3280 change fonts for these labs/facilities to red toidentify conflict, in step 3300 format display matrix and in step 3320format for export to MS Excel.

FIG. 26E illustrates a module 3340, which is a consolidated labfacilities resource management and verification event reservation outputdisplay. Lab facilities resources with conflicting schedules arehighlighted in red text for resolution.

In FIG. 27A, requirements, schedules, resources and personnel areidentified as 2140, 2160, 2180 and 2200 respectively before the machinetransformation.

In FIG. 27A, requirements, schedules, resources and personnel areidentified as 2260, 2300, 2320 and 2340 respectively after the machinetransformation.

In FIG. 27A, action item, issue and verification events are identifiedas 2220, 2240 and 2280 respectively.

Furthermore, FIG. 27A, incorporates various machine transformations,which are denoted as 1, 2, 3, 4, 7 a and 7 b.

The machine transformations denoted as 1, 2, 3 and 4 have beenillustrated in paragraphs from 254 to 262.

In FIG. 27B, risk management, pending changes, dev & waiv,giver/receiver and verification results are denoted by 2360, 2380, 2400,2420 and 2440 respectively.

FIG. 27C illustrates the machine transformation, denoted as 7 a. In FIG.27C, in step 3360 populate personnel resource database, in step 3380allocate personnel resources to events, in step 3400 select needed startand end dates and in step 3420 sort personnel.

FIG. 27D illustrates the machine transformation, denoted as 7 b. In FIG.27D, in step 3440 identify personnel where start/end dates overlap, instep 3460 change fonts for the personnel to red to identify conflict, instep 3480 format display matrix and in step 3500 format for export to MSExcel.

FIG. 27E illustrates a module 3520, which is a consolidated personnelresource management and verification event reservation output display.Personnel resources with conflicting schedules are highlighted in redtext for resolutions.

In FIG. 28A, requirements, schedules, resources and personnel areidentified as 2140, 2160, 2180 and 2200 respectively before the machinetransformation.

In FIG. 28A, requirements, schedules, resources and personnel areidentified as 2260, 2300, 2320 and 2340 respectively after the machinetransformation.

In FIG. 28A, action item, issue and verification events are identifiedas 2220, 2240 and 2280 respectively.

Furthermore, FIG. 28A, incorporates various machine transformations,which are denoted as 1, 2, 3, 4, 8 a and 8 b.

The machine transformations denoted as 1, 2, 3 and 4 have beenillustrated in paragraphs from 254 to 262.

In FIG. 28B, risk management, pending changes, dev & waiv,giver/receiver and verification results are denoted by 2360, 2380, 2400,2420 and 2440 respectively.

FIG. 28C illustrates the machine transformation, denoted as 8 a. In FIG.28C, in step 3540 populate hardware/software resource database, in step3560 allocate hardware/software resource to events, in step 3580 selectneeded start and end dates and in step 3600 sort personnel.

FIG. 28D illustrates the machine transformation, denoted as 8 b. In FIG.28D, in step 3620 identify hardware/software where start/end datesoverlap, in step 3640 change fonts for this hardware/software to red toidentify conflict, in step 3660 format display matrix and in step 3680format for export to MS Excel.

FIG. 28E illustrates a module 3700, which is a consolidated hardware andsoftware resource management and verification event reservation outputdisplay. Hardware and software resources with conflicting schedules arehighlighted in red text for resolutions.

In FIG. 29A, requirements, schedules, resources and personnel areidentified as 2140, 2160, 2180 and 2200 respectively before the machinetransformation.

In FIG. 29A, requirements, schedules, resources and personnel areidentified as 2260, 2300, 2320 and 2340 respectively after the machinetransformation.

In FIG. 29A, action item, verification events and verification resultsare identified as 2220, 2280 and 2440 respectively.

Furthermore, FIG. 29A, incorporates various machine transformations,which are denoted as 1, 2, 3, 4, 9 a and 9 b.

The machine transformations denoted as 1, 2, 3 and 4 have beenillustrated in paragraphs from 254 to 262.

In FIG. 29B, issue, risk management, pending changes, dev & waiv, andgiver/receiver are denoted by 2240, 2360, 2380, 2400 and 2420respectively.

FIG. 29C illustrates machine transformation, denoted as 9 a. In FIG.29C, in step 3720 select event to begin verification process, in step3740 select requirement to be verified, in step 3760 enter verificationreference documentation and in step 3780 check “verified” box asapplicable.

FIG. 29D illustrates machine transformation, denoted as 9 b. In FIG.29D, in step 3800 enter explanation to substantiate verification, instep 3820 link compliance artifacts to event, in step 3840 formatdisplay event verification report and in step 3860 format for export.

FIG. 29E illustrates a module 3880, which is an example output displayof results of verification events by requirement and/or complianceattributes. Actual analysis or test documentation details arehyperlinked.

In FIG. 30A, in step 3900 describes the type of system, in step 3920 ifor not an industry standard for system specification is used, in step3940 to specify how many configurations to be managed and in step 3960apply categories to the requirements.

In FIG. 30B, in step 3980 specify how many teams in a project, in step4000 if engineers are to be assigned to the specifications of theproject, in step 4020 if requirements are to be imported or to becreated within the algorithm and in step 4040 specify events to verifyrequirements, if known.

In FIG. 30C, in step 4060 assign personnel to verification events, instep 4080 specify requirement-to-event allocations, if known, in step4100 if resources to execute events to be loaded, and in step 4120 ifresources to be assigned to events.

In FIG. 30D, in step 4140, to specify when (time frame) each event to becompleted, if known and in step 4160 complete the project set up.

In FIG. 31A, requirements, schedules, resources and personnel areidentified as 2140, 2160, 2180 and 2200 respectively before the machinetransformation.

In FIG. 31A, requirements, schedules, resources and personnel areidentified as 2260, 2300, 2320 and 2340 respectively after the machinetransformation.

In FIG. 31A, action item, verification events and verification resultsare identified as 2220, 2280 and 2440 respectively.

Furthermore, FIG. 31A, incorporates various machine transformations,which are denoted as 1, 2, 3, 4, 10 a and 10 b.

The machine transformations denoted as 1, 2, 3 and 4 have beenillustrated in previous paragraphs from 254 to 262.

In FIG. 31B, issue, risk management, pending changes, dev & waiv, andgiver/receiver are denoted by 2240, 2360, 2380, 2400 and 2420respectively.

In FIG. 31C, in step 4180 create technical performance measure (TPM)list, in step 4200 update TPM status, in step 4220 link TPM measurementartifact and in step 4240 calculate TPM performance margin.

In FIG. 31D, in step 4260 perform TPM analysis and in step 4280 plot TPMperformance versus time.

FIG. 31E illustrates module a 4300, which is an example output displayof identified system and/or subsystem technical performance measuresindicating compliance to technical attributes, tolerances and margins.Such an output display of identified system and/or subsystem technicalperformance measures is tracked over a specified time span.

In FIG. 32A, in step 4320 enter system configuration(s), in step 4340enter specification(s) that apply to each configuration, in step 4360enter requirements for each specification and in step 4380 enterverification methods for each specification requirement.

In FIG. 32B, in step 4400 select specification template to be used, instep 4420 select the configuration and specification to be created andin step 4440 select “export” to create the specification.

In FIG. 33A, requirements, schedules, resources and personnel areidentified as 2140, 2160, 2180 and 2200 respectively before the machinetransformation.

In FIG. 33A, requirements, schedules, resources and personnel areidentified as 2260, 2300, 2320 and 2340 respectively after the machinetransformation.

In FIG. 33A, action item, verification events and verification resultsare identified as 2220, 2280 and 2440 respectively.

Furthermore, FIG. 33A, incorporates various machine transformations,which are denoted as 1, 2, 3, 4, 11 a and 11 b.

The machine transformations denoted as 1, 2, 3 and 4 have beenillustrated in previous paragraphs from 254 to 262.

In FIG. 33B, issue, risk management, pending changes, dev & waiv, andgiver/receiver are denoted by 2240, 2360, 2380, 2400 and 2420respectively.

In FIG. 33C, in step 4460 enter system configuration, in step 4480 enterspecification(s) that apply to each configuration, in step 4500 enterrequirement for each specification and in step 4520 enter events toverify/assess requirements.

In FIG. 33D, in step 4540 allocate requirements to events, in step 4560assign personnel to events, in step 4580 assign dates to events and instep 4600 select specification or events for plotting.

FIG. 33E illustrates a module 4620, which is an example output displaymetric of verification event-baseline plan vs. forecast vs. actual. Sucha metric of verification event is tracked over a specified time span.

FIG. 34A describes memristors in a two-dimensional configuration.Memristors are nano devices that remember information permanently,switch in nanoseconds, are super dense, and power efficient. That makesmemristors potential replacements for DRAM, flash, and disk. Memristorscan be dynamically configured on the fly to act as either memory orlogic. With memristors some block can be memory or a switching network,or logic. Memristors integrated with processing elements (e.g., CMOSprocessing elements) can enable a hybrid CMOS-memristor reconfigurablelogic.

Synapses and axons in a human brain are both effectively memristors.Memristors can mimic neurons and can enable learning or relearning basedon neural networks without supervision.

FIG. 34B describes a system on chip of memristors and hardwareprocessors in a three-dimensional configuration for learning/relearningcomputer. This is an embodiment of a system on chip based on neuralnetworks, wherein memristors and hardware processors are coupledelectrically in a three-dimensional manner to enable learning(relearning) computer to store and process massive datasets (Big Data).Various embodiments of the system on chips have been described/disclosedin “SYSTEM ON CHIP (SOC) BASED ON NEURAL PROCESSOR OR MICROPROCESSOR,U.S. patent application Ser. No. 15/530,191 Filed on Dec. 12, 2016 andin “SYSTEM ON CHIP (SOC) BASED ON PHASE TRANSITION AND/OR PHASE CHANGEMATERIAL”, U.S. Pat. No. 9,558,779, Issued on Jan. 31, 2017.

The system on chips can have Cog Ex machines/Machine OS, as an operatingalgorithm/system

System on chips, optically interconnected can enable the learning(relearning) computer to store and process massive datasets.Furthermore, the system on chips (optically interconnected) based onneural networks and a machine learning algorithm(s)/artificialintelligence based algorithm(s)/neural networks basedalgorithm(s)/neuro-fuzzy logic based algorithm(s) can enable forsupervised, unsupervised and semi-supervised learning.

It should be noted that a machine learning algorithm(s)/artificialintelligence based algorithm(s)/neural networks basedalgorithm(s)/neuro-fuzzy logic based algorithm(s) can beself-learning/relearning.

Additionally, a machine learning algorithm(s)/artificial intelligencebased algorithm(s)/neural networks based algorithm(s)/neuro-fuzzy logicbased algorithm(s) can be coupled/integrated with an algorithm(s) (e.g.,topological data analysis (TDA) or clustering algorithms) to analyze amassive set of data (e.g., Big Data).

Topological data analysis (TDA) is an approach to the analysis of alarge volume of data, utilizing techniques from topology (e.g., shape ofdatasets). Topological data analysis (TDA) can enable the geometricfeatures of a large volume of data, utilizing topology Extraction ofinformation from a large volume of data that is high-dimensional,incomplete and noisy is generally challenging. But, topological dataanalysis (TDA) provides a general framework to analyze a large volume ofdata in a manner that is insensitive to the particular metric chosen andprovides dimensionality reduction and robustness to noise. One of theadvantages of topological analysis is low dimensional representation ofhigher dimensional connectivity.

Topological data analysis (TDA) coupled/integrated with a machinelearning algorithm(s)/artificial intelligence based algorithm(s)/neuralnetworks based algorithm(s)/neuro-fuzzy logic based algorithm(s) canenable to spot/analyze/learn (a) patterns in a large volume of data(that would have been impossible to identify using traditionalstatistical methods), (b) segments in a large volume of data on manylevels, (c) texts, images and sensors' data, (d) complex dependencies ina large volume of data without a supervision

Clustering algorithms are powerful meta-learning tool to accuratelyanalyze a large volume of data. In particular, they can be utilized tocategorize data into clusters such that objects, which are grouped inthe same cluster when objects are similar according to specific metrics.

Furthermore, game theory is an excellent tool to integrate withrequirement, compliance and resource management algorithm, at least foraccounting for conflict in the requirement input data or complianceinput data.

A project can be conceived as a single continuum or recurringnegotiations with multiple participants with varying concerns. Gametheory can be classified into two categories: (a) non-cooperative game,where a decision-making unit treats the other participants ascompetitors and (b) a cooperative game, where a group of decision-makingunits decide to undertake a project together in order to achieve theirshared business objectives.

In game theory, individuals/groups/units become players, when theirrespective decisions coupled with the decisions made by other players,produce an outcome/output. The options available to players to bringabout particular outcomes are called as strategies, which are linked tooutcomes/outputs by a mathematical function that specifies theconsequences of the various combinations of strategy choices by the allplayers in a game. A coalition refers to the formation of sub-sets ofplayers' options under coordinated strategies. In game theory, the coreis the set of feasible allocations that cannot be improved upon by acoalition. An imputation X={x₁, x₂ . . . x_(n)} is in the core of ann-person game if and only if for each subset, S of N:

${\sum\limits_{i = 1}^{n}x_{i}} \geq {V(S)}$where V(s) is the characteristic function V of the subset S indicatingthe amount (reward) that the members of S can be sure of receiving, ifthey act together and form a coalition (or the amount of S can getwithout any help from players who are not in S). Above equation statesthat an imputation x is the core (that X is undominated), if and only iffor every coalition S, the total of the received by the players in S(according to X) is at least as large a V(S). The core can also bedefined by the equation below as the set of stable imputations:

$C\text{:}\left\{ {{x = {{\left( {x_{1},\ldots\mspace{14mu},x_{n}} \right)\text{:}{\sum\limits_{i \in N}x_{i}}} = {{{V(N)}\mspace{14mu}{and}\mspace{14mu}{\sum\limits_{i \in S}x_{i}}} \geq {V(S)}}}},{\forall{S \Subset N}}} \right\}$The imputation x is unstable through a coalition S, if the equationbelow is true, otherwise is stable.

${V(S)} > {\sum\limits_{i \in S}x_{i}}$The core can consist of many points. The size of the core can be takenas a measure of stability or how likely a negotiated agreement is proneto be upset. To determine the maximum penalty (cost) that a coalition inthe network can be sure of receiving, the linear programming problemrepresented by the equation below can be used, when maximize

x₁ + x₂ + x₃ + … + x_(n)${\sum\limits_{i \in S}x_{i}} \leq {{V(C)}{\forall{C \Subset {{N\mspace{14mu}{subject}\mspace{14mu}{to}\mspace{14mu}\left( {x_{1},x_{2},\ldots\mspace{14mu},x_{n}} \right)} \geq 0}}}}$

Thus, as outlined above, a game theory based algorithm can account forany conflict in the requirement input data or compliance input data.

A blockchain is a global distributed ledger/database running on millionsof devices and open to anyone, where not just information, but anythingof value. In essence it is a shared, trusted public ledger that everyonecan inspect, but which no single user controls. A blockchain creates adistributed document of (outputs/transactions) in a form of a digitalledger, which can be available on a network of computers. When atransaction happens, the users propose a record to the ledger. Recordsare bundled into blocks (groups for processing) and each block receivesa unique fingerprint derived from the records it contains. Each blockincludes the fingerprint of the prior block, creating a robust andunbreakable chain. It's easy to verify the integrity of the entire chainand nearly impossible to falsify historic records. In summary, ablockchain is a public ledger of transactions, which critically providestrust, based upon mathematics rather than humanrelationships/institutions.

Public blockchain: a public blockchain is a blockchain that anyone inthe world can read, anyone in the world can send transactions to andexpect to see them included if they are valid, and anyone in the worldcan participate in the consensus process—the process for determiningwhat blocks get added to the chain and what the current state is.

Consortium blockchain: a consortium blockchain is a blockchain where theconsensus process is controlled by a pre-selected set of nodes; forexample, one might imagine a consortium of 20 units (e.g., companies),each of which operates a node and of which 20 must sign every block inorder for the block to be valid. The right to read the blockchain may bepublic, or restricted to the participants, and there are also hybridroutes such as the root hashes of the blocks being public together withan API that allows members of the public to make a limited number ofqueries and get back cryptographic proofs of some parts of theblockchain state. These blockchains may be considered “partiallydecentralized”.

Private blockchain: a private blockchain is a blockchain where writepermissions are kept centralized to one organization. Read permissionsmay be public or restricted to an arbitrary extent. Likely applicationsinclude database management, auditing, etc internal to a single company,and so public readability may not be necessary in many cases at all,though in other cases public auditability is desired.

A public blockchain or a consortium blockchain or a private blockchainis an excellent tool for compliance and it can be integrated with therequirement, compliance and resource management algorithm, utilizing anapplication programming interface, at least for:

-   -   a requirement or a requirement input data from a data source or        an inputting device,    -   a compliance input data from a data source or an inputting        device,    -   a resource (e.g., a hardware resource, a software resource, a        human resource and a financial resource),    -   a distributed document (e.g., the specification output) and its        past revisions, which are generated by the requirement,        compliance and resource management algorithm.

In the above disclosed specifications “I” has been used to indicate an“or”.

PREFERRED EMBODIMENTS OF SPECIFICATIONS

Any example in the above disclosed specifications is by way of anexample only and not by way of any limitation. The best mode requirement“requires an inventor to disclose the best mode contemplated by him/her,as of the time he/she executes the application, of carrying out theinvention.” “ . . . [T]he existence of a best mode is a purelysubjective matter depending upon what the inventor actually believed atthe time the application was filed.” See Bayer AG v. ScheinPharmaceuticals, Inc. The best mode requirement still exists under theAmerica Invents Act (AIA). At the time of the invention, the inventordescribed preferred best mode embodiments of the present invention. Thesole purpose of the best mode requirement is to restrain the inventorfrom applying for a patent, while at the same time concealing from thepublic preferred embodiments of their inventions, which they have infact conceived. The best mode inquiry focuses on the inventors' state ofmind at the time he/she filed the patent application, raising asubjective factual question. The specificity of disclosure required tocomply with the best mode requirement must be determined by theknowledge of facts within the possession of the inventor at the time offiling the patent application. See Glaxo, Inc. v. Novopharm LTD., 52F.3d 1043, 1050 (Fed. Cir. 1995). The above disclosed specifications arethe preferred best mode embodiments of the present invention. However,they are not intended to be limited only to the preferred best modeembodiments of the present invention. Numerous variations and/ormodifications are possible within the scope of the present invention.Accordingly, the disclosed preferred best mode embodiments are to beconstrued as illustrative only. Those who are skilled in the art canmake various variations and/or modifications without departing from thescope and spirit of this invention. The inventors of the presentinvention are not required to describe each and every conceivable andpossible future embodiment in the preferred best mode embodiments of thepresent invention. See SRI Intl v. Matsushita Elec. Corp. of America,775F.2d 1107, 1121, 227 U.S.P.Q. (BNA) 577, 585 (Fed. Cir. 1985)(enbanc). Accordingly, the disclosed preferred best mode embodiments areto be construed as illustrative only. Those who are skilled in the artcan make various variations and/or modifications without departing fromthe scope and spirit of this invention. The inventors of the presentinvention are not required to describe each and every conceivable andpossible future embodiment in the preferred best mode embodiments of thepresent invention. See SRI Intl v. Matsushita Elec. Corp. of America,775F.2d 1107, 1121, 227 U.S.P.Q. (BNA) 577, 585 (Fed. Cir. 1985)(enbanc).

SCOPE AND SPIRIT OF THE PRESENT INVENTION

The scope and spirit of this invention shall be defined by the claimsand the equivalents of the claims only. The exclusive use of allvariations and/or modifications within the scope of the claims isreserved. Unless a claim term is specifically defined in the preferredbest mode embodiments, then a claim term has ordinary meaning, asunderstood by a person with an ordinary skill in the art, at the time ofthe present invention. As noted long ago: “Specifications teach. Claimsclaim”. See Rexnord Corp. v. Laitram Corp., 274 F.3d 1336, 1344 (Fed.Cir. 2001). The rights of claims (and rights of the equivalents of theclaims under the Doctrine of Equivalents-meeting the “Triple IdentityTest” (a) performing substantially the same function, (b) insubstantially the same way and (c) yielding substantially the sameresult. See Crown Packaging Tech., Inc. v. Rexam Beverage Can Co., 559F.3d 1308, 1312 (Fed. Cir. 2009)). Claims of the present invention arenot narrowed or limited by the selective import of the specifications(of the preferred embodiments of the present invention) into the claims.The term “means” was not used nor intended nor implied in the disclosedpreferred best mode embodiments of the present invention. Thus, theinventor has not limited the scope of the claims as mean plus function.Furthermore, the scope and spirit of the present invention shall bedefined by the claims and the equivalents of the claims only.

We claim:
 1. A method of requirement, compliance and resource managementalgorithm utilizes: a learning computer system, wherein the learningcomputer system comprises: a premise computer system, a mobile computersystem and a cloud computer system, wherein the learning computer systemfurther comprises: one or more hardware processors or system on chipsbased on neural networks in communication with a computer readablemedium, wherein the computer readable medium stores one or more softwaremodules, including instructions for the method of requirement,compliance and resource management algorithm and one or more machinelearning algorithms, that are executable by the one or more hardwareprocessors or system on chips based on neural networks, wherein themethod of requirement, compliance and resource management algorithmcomprises: steps (a), (b), (c), (d), (e), (f), (g), (h), (i), (j), (k)and (l), at least in an ordered manner, (a) a requirement inputcollection algorithm or a set of step-by-step instructions forcollecting a requirement or a requirement input data from a data sourceor an inputting device; (b) a compliance requirement input collectionalgorithm or a set of step-by-step instructions for collectingcompliance or a compliance input data from a data source or an inputtingdevice; (c) a requirement analysis algorithm or a set of step-by-stepinstructions for analyzing the requirement, the requirement input data,the compliance or the compliance input data; (d) a specificationgeneration algorithm or a set of step-by-step instructions forgenerating a specification of the requirement based on the analysis ofthe requirement, the requirement input data, the compliance or thecompliance input data; (e) a resource allocation algorithm or a set ofstep-by-step instructions for allocating a resource, wherein theresource consisting of: a hardware resource, a software resource, ahuman resource and a financial resource; (f) a verification algorithm ora set of step-by-step instructions for verifying the requirement, therequirement input data, the compliance or the compliance input data; (g)a fuzzy logic algorithm or a set of step-by-step instructions foraccounting for inexactness of the requirement input data or inexactnessof interpretation of the requirement input data; (h) a statisticalanalysis algorithm or a set of step-by-step instructions for accountingfor variability of the requirement input data or variability ofinterpretation of the requirement input data; (i) a game theory basedalgorithm or a set of step-by-step instructions for accounting forconflict in the requirement input data or compliance input data; (j)interfacing at least to search for a keyword, utilizing a graphical userinterface; (k) searching for the keyword, utilizing the graphical userinterface; and (l) a traceability generation algorithm or a set ofstep-by-step instructions for tracing the requirement input data or therequirement output data, wherein the method of requirement, complianceand resource management algorithm is further interfacing with a softwareprogram or an algorithm for analysis of a large set of data or ablockchain, utilizing an application programming interface.
 2. Themethod of requirement, compliance and resource management algorithm inclaim 1, is further coupled with a set of instructions for collectingthe requirement or the requirement input data in a question and answerformat.
 3. The method of requirement, compliance and resource managementalgorithm in claim 1, further comprising: a set of weighting logicinstructions for estimating importance of the requirement input data. 4.The method of requirement, compliance and resource management algorithmin claim 1, further comprising: a neuro-fuzzy logic algorithm or a setof step-by-step instructions to account for inexactness of an outputdata of the requirement.
 5. The method of requirement, compliance andresource management algorithm in claim 1, further comprising: aneuro-fuzzy logic algorithm or a set of step-by-step instructions toaccount for inexactness of interpretation of an output data of therequirement.
 6. The method of requirement, compliance and resourcemanagement algorithm in claim 1, further comprising: a Monte Carlomethod based algorithm or a set of step-by-step instructions.
 7. Themethod of requirement, compliance and resource management algorithm inclaim 1, further comprising: a set of constraint analysis instructionsfor an assessment of constraints of the requirement.
 8. The method ofrequirement, compliance and resource management algorithm in claim 1, isfurther coupled with a set of instructions for identifying a risk, whenthe requirement changes.
 9. The method of requirement, compliance andresource management algorithm in claim 1, is further coupled with a setof instructions for collaboration between users.
 10. A method ofrequirement, compliance and resource management algorithm utilizes: alearning computer system, wherein the learning computer systemcomprises: a premise computer system, a mobile computer system and acloud computer system, wherein the learning computer system furthercomprises: one or more hardware processors or system on chips based onneural networks in communication with a computer readable medium,wherein the computer readable medium stores one or more softwaremodules, including instructions for the method of requirement,compliance and resource management algorithm and one or more machinelearning algorithms, that are executable by the one or more hardwareprocessors or system on chips based on neural networks, wherein themethod of requirement, compliance and resource management algorithmcomprises: steps, (a), (b), (c), (d), (e), (f), (g), (h), (i), (j) and(k), at least in an ordered manner, (a) a requirement input collectionalgorithm or a set of step-by-step instructions for collecting arequirement or a requirement input data from a data source or aninputting device; (b) a requirement analysis algorithm or a set ofstep-by-step instructions for analyzing the requirement or therequirement input data; (c) a specification generation algorithm or aset of step-by-step instructions for generating a specification of therequirement based on the analysis of the requirement or the requirementinput data; (d) a resource allocation algorithm or a set of step-by-stepinstructions for allocating a resource, wherein the resource consistingof: a hardware resource, a software resource, a human resource and afinancial resource; (e) a verification algorithm or a set ofstep-by-step instructions for verifying the requirement or therequirement input data; (f) a fuzzy logic algorithm or a set ofstep-by-step instructions for accounting for inexactness of therequirement input data or inexactness of interpretation of therequirement input data; (g) a statistical analysis algorithm or a set ofstep-by-step instructions for accounting for variability of therequirement input data or variability of interpretation of therequirement input data; (h) a game theory based algorithm or a set ofstep-by-step instructions for accounting for conflict in the requirementinput data or compliance input data; (i) interfacing at least to searchfor a keyword, utilizing a graphical user interface; (j) searching forthe keyword, utilizing the graphical user interface; and (k) atraceability generation algorithm or a set of step-by-step instructionsfor tracing the requirement input data or the requirement output data,wherein the method of requirement, compliance and resource managementalgorithm is further interfacing with a software program or an algorithmfor analysis of a large set of data or a blockchain, utilizing anapplication programming interface.
 11. The method of requirement,compliance and resource management algorithm in claim 10, is furthercoupled with a set of instructions for collecting the requirement or therequirement input in a question and answer format.
 12. The method ofrequirement, compliance and resource management algorithm in claim 10,further comprising: a set of weighting logic instructions for estimatingimportance of the requirement input data.
 13. The method of requirement,compliance and resource management algorithm in claim 10, furthercomprising: a neuro-fuzzy logic algorithm or a set of step-by-stepinstructions to account for inexactness of an output data of therequirement.
 14. The method of requirement, compliance and resourcemanagement algorithm in claim 10, further comprising: a neuro-fuzzylogic algorithm or a set of step-by-step instructions to account forinexactness of interpretation of an output data of the requirement. 15.The method of requirement, compliance and resource management algorithmin claim 10, further comprising: a Monte Carlo method based algorithm ora set of step-by-step instructions.
 16. The method of requirement,compliance and resource management algorithm in claim 10, furthercomprising: a set of constraint analysis instructions for an assessmentof constraints of the requirement.
 17. The method of requirement,compliance and resource management algorithm in claim 10, is furthercoupled with a set of instructions for identifying a risk, when therequirement changes.
 18. The method of requirement, compliance andresource management algorithm in claim 10, is further coupled with a setof instructions for collaboration between users.
 19. A method ofrequirement, compliance and resource management algorithm utilizes: alearning computer system, wherein the learning computer systemcomprises: a premise computer system, a mobile computer system and acloud computer system, wherein the learning computer system furthercomprises: one or more hardware processors or system on chips based onneural networks in communication with a computer readable medium,wherein the computer readable medium stores one or more softwaremodules, including instructions for the method of requirement,compliance and resource management algorithm and one or more machinelearning algorithms, that are executable by the one or more hardwareprocessors or system on chips based on neural networks, wherein themethod of requirement, compliance and resource management algorithmcomprises: steps, (a), (b), (c), (d), (e), (f), (g), (h), (i), (j) and(k), at least in an ordered manner, (a) a requirement input collectionalgorithm or a set of step-by-step instructions for collecting arequirement or a requirement input data from a data source or aninputting device; (b) a requirement analysis algorithm or a set ofstep-by-step instructions for analyzing the requirement or therequirement input data; (c) a specification generation algorithm or aset of step-by-step instructions for generating a requirement of thespecification based on the analysis of the requirement or therequirement input data; (d) a resource allocation algorithm or a set ofstep-by-step instructions for allocating a resource, wherein theresource consisting of: a hardware resource, a software resource, ahuman resource and a financial resource; (e) a verification algorithm ora set of step-by-step instructions for verifying the requirement or therequirement input data; (f) a neuro-fuzzy logic algorithm or a set ofstep-by-step instructions for accounting for inexactness of therequirement input data or inexactness of interpretation of therequirement input data; (g) a statistical analysis algorithm or a set ofstep-by-step instructions for accounting for variability of therequirement input data or variability of interpretation of therequirement input data; (h) a game theory based algorithm or a set ofstep-by-step instructions for accounting for conflict in the requirementinput data or compliance input data; (i) interfacing at least to searchfor a keyword, utilizing a graphical user interface; (j) searching forthe keyword, utilizing the graphical user interface; and (k) atraceability generation algorithm or a set of step-by-step instructionsfor tracing the requirement input data or the requirement output data,wherein the method of requirement, compliance and resource managementalgorithm is further interfacing with a software program or an algorithmfor analysis of a large set of data or a blockchain, utilizing anapplication programming interface.
 20. The method of requirement,compliance and resource management algorithm in claim 19, is furthercoupled with a set of instructions for collecting the requirement or therequirement input in a question and answer format.
 21. The method ofrequirement, compliance and resource management algorithm in claim 19,further comprising: a set of weighting logic instructions for estimatingimportance of the requirement input data.
 22. The method of requirement,compliance and resource management algorithm in claim 19, furthercomprising: a Monte Carlo method based algorithm or a set ofstep-by-step instructions.
 23. The method of requirement, compliance andresource management algorithm in claim 19, further comprising: a set ofconstraint analysis instructions for an assessment of constraints of therequirement.
 24. The method of requirement, compliance and resourcemanagement algorithm in claim 19, is further coupled with a set ofinstructions for identifying a risk, when the requirement changes. 25.The method of requirement, compliance and resource management algorithmin claim 19, is further coupled with a set of instructions forcollaboration between users.